Welcome to Reasoned Writing.
The overall goal of Reasoned Writing is to help content-focused curricula use scientific reasoning and communication for learning. The module is based on three general hypotheses:
1) Critical Understanding is central to scientific thinking and an important component of science education (Boyer et al., 1998).
2) Writing can help develop Critical Understanding skills (analysis, synthesis, and evaluation; Cavdar and Doe, 2012; Quitadamo and Kurtz, 2007).
3) Using consistent frameworks based on clear and specific elements facilitates both critical understanding and clear writing (National Academies of Science, 2000).
Reasoned Writing (RW) focuses on three specific objectives (the "Three Ss," if you will). The first (and most important) objective is STRUCTURE. Specifically, RW seeks to help writers create appropriate FRAMEWORKS to structure arguments (National Academies of Science, 2000). Straightforward, reasoned frameworks can help structure papers, paragraphs, and sentences. The second objective of RW is to encourage clear reasoning through SIMPLE presentation. Simple presentations limit the amount of information audiences must consider at any one time. The final objective of RW is to encourage scientific communicators to reason and communicate more SPECIFICALLY. Specific communication is self-contained, unambiguous, and truthful.
The companion module "A Framework for Scientific Papers" applies the general principle of using simple, specific frameworks to the particular case of writing more clearly reasoned hypothesis-based scientific papers.
To continue, follow the links to each area or use one of the suggested approaches for navigating the module.
Reasoned Writing / A Framework for Scientific Papers
© 2018, Devin Jindrich
All rights reserved.
Why "Reasoned Writing?"
"Reasoned Writing" (RW) and "A Framework for Scientific Papers" (AFSP) are intended to help students more clearly reason and communicate.
Instead of logic and probability, students (and even many professionals) often rely on biased guesses to make conclusions (Tversky and Kahneman, 1974). However, clear and systematic reasoning is an important component of the scientific method (Platt, 1964). For students, using the scientific method involves active learning that integrates skill and content learning (Michael, 2006). The challenge of scientific discovery facilitates "transformational" education, where both instructors and students collaborate to make discoveries (Full et al., 2015; Slavich and Zimbardo, 2012). Therefore, opportunities to discuss and use systematic reasoning and the scientific method can be an important part of science education.
However, some areas of science education (e.g. Biology) often focus on rote learning, with relatively fewer opportunities for students to practice higher-level analytical and evaluative thinking (Zheng et al., 2008; Arum and Roksa, 2010). Therefore, some science curricula may benefit from using the scientific method in more class activities. The RW and AFSP modules seek to help instructors and students apply scientific reasoning and methods to a specific aspect of science: written and spoken communication.
Scientific communication applies reasoning to an important practical application.
Written (and spoken) communication provides opportunities for students to understand and apply scientific reasoning. Effective communication involves high-level thinking such as synthesis and evaluation (Bloom, 1956; Rochford and Borchert, 2011). Synthesis and evaluation are important components of critical thinking (Gronlund, 2004). Moreover, communication consistently ranks as one of the most important workplace skills (Bloomberg, 2016, Forbes, 2014, GMAT, 2017). However, according to employers, colleges do not sufficiently develop both written and spoken communication (Casner-Lotto, 2006; Chronicle of Higher Education, 2012). Therefore, communication can allow students to learn and apply scientific reasoning while developing valuable skills.
Students may benefit from a structured approach to scientific reasoning and communication.
How can students develop communication skills that improve reasoning? In many cases, students are told to use reasoning and argumentation, but not provided guidance on HOW to construct reasoned arguments (Hillocks, 2010; Heijltjes, 2014). At the undergraduate and graduate level, students are often expected to learn scientific reasoning and writing informally by reading scientific literature (Lin, 1989). However, reading primary literature typically confronts students with new terminology, new concepts, and an unfamiliar written format. Moreover, not all scientific papers employ strong, explicit reasoning (Platt, 1964). Therefore, although reading and emulating scientific literature is undoubtedly useful, being able to use scientific papers to strengthen reasoning and writing is challenging and can take years.
A structured approach can facilitate and expedite the development of scientific reasoning and communication (Greene, 2010). Many academic papers and informal publications provide guidance for scientific writing (Brand, 2001; Brand, 2008; Jirge, 2017; Singh and Mayer, 2014). However, many academic papers and resources about writing are intended for practicing scientists, not for students. For example, Dick Brand's excellent 2001 paper writes "Imagine critical observations as premises of Aristotelian logic leading to a conclusion: If a; and b; and c; and d; then we logically conclude x; or y; or z (where x; y; or z represent hypotheses to be tested)" (Brand, 2001). Although scientists may be familiar with Aristotelian logic, students may not have sufficient background to feel comfortable using logic to structure writing. Therefore, students may benefit from more accessible guidance to help them develop scientific reasoning and writing.
Many accessible books on writing (and specifically scientific writing) are available to instructors and students (Alley, 2008; Booth et al., 2016; Greene, 2010; Green and Lawlor, 2017). Moreover, students have access to online resources for writing (e.g. University of Richmond's Writer's Web, The Purdue OWL, Trent University, etc.). However, strong reasoning is necessary for effective scientific communication. In my estimation, few textbooks or online sources sufficiently address how structured reasoning is essential for effective scientific writing (exceptions include Moriarty, 2007). As far as I can determine, students do not have sufficient access to textbooks or online resources that use reasoning to improve writing (and vice versa).
The process of creating frameworks can improve understanding and communication.
Over the course of their academic careers, students are encouraged to use many frameworks to help understand different subjects. However, using insufficiently-explained rules and frameworks can turn writing into a process of imitation rather than of understanding and creation (Warner, 2018). In my experience, students greatly benefit from creating their own frameworks. The process of creation helps students understand the connections among concepts and information. I find that students are also more invested in frameworks that they create (compared to frameworks that they are given), making it more likely that they will use the frameworks in the future. Therefore, the process of creating conceptual and graphical reasoned frameworks is an important part of the Reasoned Writing approach.
Several barriers may hamper the use of reasoning and writing in science courses.
I hypothesize that several structural barriers may limit the use of the resources (reasoning and writing textbooks/websites) that are available to students, including (but not limited to):
1) Lack of time dedicated to reasoning skills. Science curricula often do not or cannot include dedicated courses for critical reasoning, research methods, and scientific writing (Brand and Huiskes, 2001;Coil et al., 2010; Turbek et al., 2016). Moreover, textbooks and courses on reasoning are often separate from textbooks and courses on writing. Covering scientific reasoning and writing in courses dedicated to other topics may be cost- and time-prohibitive without support (Moskovitz and Kellogg, 2011). Alternatively, addressing reasoning and writing in dedicated courses may also not be the most effective approach to learning (Hattie and Donoghue, 2016; Willingham, 2019). Therefore, an objective of RW/AFSP is to provide support for reasoned communication in the most time-efficient format possible so that the module can be integrated into non-writing courses and labs.
2) Too much information. I hypothesize that existing resources often present instructors and students with too much information. For example, textbooks and other resources about writing often survey a broad range of writing styles instead of focusing on one type of writing (e.g. reasoned arguments; Zinsser, 2006). Textbooks on rhetoric often survey many forms of persuasion in addition to the data-based reasoning used in science (Ramage et al., 2016). Even for reasoned arguments, different authors recommend a range of frameworks (Henderson et al., 2018). For example, basic elements of writing such as structuring paragraphs are the subject of many frameworks, often with overlapping instructions (TEEL, TAXES, AXES, PEEL, TIPTOP, SEED, PIE, etc. etc.). Moreover, many resources for critical thinking and logic are not specific to science and/or potentially very complex (Howson and Urbach, 2005; Moore and Parker, 2017). Therefore, the objective of RW/AFSP is not to widely survey different forms of communication. The objective of RW/AFSP is also not to comprehensively review logic, rhetoric, linguistics, and scientific methodology. Instead, the objective of RW/AFSP is to SIMPLIFY the process of developing reasoned arguments as much as possible. Therefore, RW/AFSP encourages students to create frameworks based on ONE basic structure that can be generalized to different contexts. The structure of Aristotelian logic and the "Classical" rhetorical format used by RW/AFSP is a widely consistent component of many scientific disciplines.
3) Underrepresentation of science in core writing courses. Many resources available to instructors to support critical thinking and writing do not focus on scientific communication. For example, many introductory writing courses and campus writing resources use English literature or other non-scientific writing as course material (Timmerman et al, 2010). Students may not have access to training in scientific writing before being expected to write laboratory reports or other scientific communication. Therefore, the objective of RW/AFSP is to augment writing instruction and quickly improve reasoning and technical writing skills.
Several common practices may detract from teaching scientific reasoning and communication.
In addition to structural barriers, I hypothesize that several common practices also contribute to difficulties with reasoned communication among students:
1) APA or MLA Formatting. In speaking with students over the past 10 years, one theme has consistently emerged. Students convey that undergraduate writing courses emphasize formatting documents to specific guidelines, often using APA style. In my estimation, including formatting requirements comes at the expense of students actually learning to write. I hypothesize that teaching formatting is actually detrimental to writing. Yes, formatting is a type of structure, and attention to detail is one important component of science (Holstein et al., 2015). However, attention to detail is not nearly as important as reasoning for science (Platt, 1964). Moreover, requiring students to structure the least relevant aspects of papers (i.e. margins, bibliographies, etc.) substantially detracts from students being able to structure the most important parts of their paper (i.e. main arguments and everything else).
I hypothesize that when the relatively easy task of formatting is as much a component of assessment as the much more difficult process of constructing strong arguments, students will reasonably focus on formatting. Therefore, formatting is explicitly NOT a part of RW/AFSP, because formatting is NOT writing. Personally, I provide only general guidance about formatting unless absolutely necessary. In my experience, undergraduate students are sufficiently skilled at formatting, and enforcing detailed rules for formatting simply distracts from learning how to effectively write.
2) Inconsistent frameworks. In addition to being overwhelmed by information, students often report that they are confused by inconsistent or conflicting guidance on scientific communication. For example, students may be told to use an argumentative format for the Introduction, a chronological format for the Methods, a descriptive format for the Results, then back to an argumentative format for the Discussion of a scientific paper. Students are instructed to use topic sentences or in-text citations to structure paragraphs while referring to books or papers that do not use topic sentences or in-text citations themselves. Granted, there are many ways of doing science and of science writing, and not a single correct approach. However, in my experience, students benefit from reducing (simplifying) the number of frameworks that they are expected to use when writing. Therefore, in RW/AFSP, I focus on encouraging students to use a SINGLE framework (reasoned argument) to structure most aspects of their communication.
3) Flat guidance structure. Not all aspects of arguments are equally important. However, in my estimation, many instructional materials do not sufficiently emphasize the power of hierarchical frameworks (Dumont, 2009). For example, in many frameworks, elements such as examples are presented at the same level as quantitative evidence (instead of more appropriately in a supportive role). Therefore, in RW/AFSP I make arguments for using hierarchies to strengthen reasoning and communication. Moreover, RW/AFSP seeks to use a hierarchical framework itself to emphasize more important aspects of reasoning and writing.
Reasoned Writing is intended to help students use deliberate, evidence-based practice strategies towards attainable goals.
How can instructors use the RW and AFSP modules to encourage learning? Clearly, there is no simple answer, and exposing students to diverse teaching styles is likely to contribute to the ability of students to transfer and apply skills and knowledge. However, appropriate goal-setting, organizing practice, and providing informative feedback may make it easier for students to learn new information and skills.
Focusing attention on practice (performing "deliberate" practice) towards achieving defined goals can contribute to learning (Ericsson, 2017). The most effective goals are attainable, specific and challenging (Locke and Latham, 2006). In my estimation, an important role for instructors is to determine appropriate goals based on the individual and collective needs of their students. The material in the RW module is intended to be conceptually challenging, but provide concrete goals for using simple, specific, reasoned frameworks to structure different sections of scientific papers.
Instructors can also consider evidence-based learning strategies for content learning. Students can benefit from using frameworks to alternate content presentation with opportunities for practice (Rosenshine, 2012). Interleaving, or other forms of "spacing" practice by providing rest breaks between learning trials, can provide time for reflection and contribute to learning (Son and Simon, 2012). Using "desirable difficulties" such as non-repetitive practice can result in more learning than repetitive blocked practice (Dobson, 2011; Rohrer and Pashler, 2010). Frequent testing and re-testing (self-testing or otherwise) can also enhance learning (Karpicke and Grimaldi, 2012). Providing informative, but not excessive, feedback can also contribute to learning (Hayes et al., 2010; Winstein and Schmidt, 1990). Therefore, RW allows for different approaches to the content, to help instructors select learning strategies appropriate for both students and other class constraints.
One goal of "Reasoned Writing" and "A Framework for Scientific Papers" is to support courses whose primary focus is NOT writing, but content (e.g. laboratories, courses that use case studies, etc.; Full et al, 2015). Therefore, RW and AFSP seek to be flexible and adaptable to many different types of courses. RW and AFSP seek to provide many options for navigating the modules and approaching the material. My hope is that instructors will be able to incorporate information from each module into course activities in a non-linear manner. I welcome suggestions for alternative paths (or any other suggestions, really).
Reasoned Writing / A Framework for Scientific Papers seeks to use simple, consistent frameworks to quickly improve reasoned thinking and communication. My expectation is that the RW / AFSP modules will be strongest when included in courses that involve substantial application, repetition, and feedback (e.g. laboratories or courses based on written case studies), which will allow students to practice and improve their skills.
ABOUT THE AUTHOR
Devin Jindrich is an Associate Professor of Kinesiology at California State University, San Marcos, where he directs the Laboratory for Integrative Motor Behavior (LIMB) lab. Research in the LIMB Lab focuses on the interactions between biomechanics and motor control that result in effective movement, or “neuromechanics.” We seek to advance our fundamental understanding of how biomechanical and neural systems interact during movement, and apply neuromechanical principles to biomedical applications. Whereas simple mechanical models can describe important aspects of constant-speed forward locomotion, the mechanics and control of maneuvering (changing movement direction) or remaining stable (maintaining a desired movement direction) are less well understood. Consequently, we investigate the mechanisms used by insects, humans, and other animals to maneuver and remain stable during rapid locomotion, towards developing a general framework for understanding the control of maneuverability and stability. The results of these experiments support the hypothesis that musculoskeletal design and physiology can simplify the control requirements for maneuvering and remaining stable. A second focus of the LIMB lab is on using neuromechanics to prevent injuries. We use experimental studies and computer simulations to assess the potential for injuries associated with emerging multitouch computer input devices, with the ultimate goal of helping designers create sets of multitouch gestures that minimize future injury risk. Neuromechanical principles can also make important contributions to improving motor function following injuries. Using rodent and primate animal models, we use neuromechanical techniques to develop more effective therapies and technological interventions for restoring function after neuromotor injury.
Dr. Jindrich has authored over 37 peer-reviewed publications in established scientific journals, in addition to numerous successful grant applications and conference presentations. Dr. Jindrich has over 10 years of diverse teaching experiences at both research universities (U.C. Berkeley, Arizona State University) and teaching/research institutions (CSU San Marcos). He has mentored undergraduate, Master's, and Ph.D. students at U.C. Berkeley, Harvard School of Public Health, UCLA, Arizona State University, and CSU San Marcos. Dr. Jindrich has successfully implemented the principles of Reasoned Writing / A Framework for Scientific Papers in several content-based courses (Motor Control and Biomechanics). With guidance and feedback, he has observed dramatic improvements in student reasoning and writing over the course of a single semester.File: 1
WHY are frameworks important? Strong frameworks can contribute both to learning and to effective communication.
DEFINITION: What is a "framework?" A framework is a structure of assumptions, facts, and rules that are connected to support a concept (National Research Council. 2000).
Frameworks can help to connect new information to previous knowledge, which facilitates content learning (National Research Council. 2000).
Frameworks can help to structure communication between authors and audiences (Booth, 2016). Using a framework to structure communication makes the communication explanatory, instead of solely descriptive. Explanatory communication places information in context, which facilitates understanding and retention (National Research Council. 2000).
Therefore, frameworks can help individuals learn for themselves and also effectively communicate with others. Structure is one key to clarity despite complexity.
Many types of frameworks are available. However, not all frameworks are equally strong.
Frameworks can structure ideas in many different ways (Handlesman, 2007). For example, exercises such as creating concept maps, Venn diagrams, or comparing and contrasting ideas all use frameworks to help structure information and contribute to learning (Handlesman, 2007). Visual representations of frameworks can help to analyze complex problems such as designing scientific experiments. However, not all frameworks are equally useful for organizing thinking and writing.
Deliberately selecting a specific framework for every aspect of communication can clarify both thinking and writing. Three frameworks that are available to structure writing are: chronological (time-based), lists, and reasoned.
Concept Maps are graphical frameworks that link elements of information with a variety of possible relationships. For example, the concept of "premises" (units of information of a reasoned argument) could be illustrated using a map:
Concept maps can be useful when information can be clarified using a minimally-structured spatial organization, and does not easily fit into more structured frameworks such as hierarchicies.
Venn Diagrams are structured representations of relationships, and commonly used in logic (Layman, 2005). In a Venn Diagram, each area (e.g. circles in the diagram below) represents a set of elements that share a common attribute. Overlapping areas indicate elements that share attributes of two or more areas. For example, sound scientific conclusions can be represented as a Venn Diagram:
Chronologies are useful frameworks to structure communication when TIME is the most important variable.
DEFINITION: Chronologies use TIME as a framework to connect elements of information.
For example, a timeline is a graphical framework that can illustrate important time-based information.
Chronologies are useful in contexts where time is the most important variable. For example, chemical reactions, laboratory techniques, or clinical interventions may critically depend on time (Bonner, 2013). A chronological framework is therefore appropriate to describe the steps necessary for a desired outcome. Time may also be important in situations such as court proceedings, where a chronological framework may help establish causality (Brooks, 2016). Therefore, chronological frameworks are appropriate in contexts where time is critical for linking factual information together.
However, a chronology is NOT the strongest framework to use in many academic and scientific contexts. For example, time is not the primary variable in hypothesis-testing scientific publications. Although time may be important for some aspects of study design, scientists typically test hypotheses using statistical tests and logic (Platt, 1964). Moreover, many students employ chronologies for academic or career communications when other frameworks would be more effective. For example, the purpose of documents such as personal statements and cover letters is typically to provide evidence that a candidate has the attributes and skills necessary to succeed in a specific field. Programs ask for "personal," not "confessional" statements: strong personal statements are business propositions, not life stories. The chronological frameworks often used by students to demonstrate motivation based on past experiences do NOT effectively satisfy the objectives of the "business proposition": demonstrating specific capabilities that will enable future success.
Therefore, although chronologies can be useful in specific contexts, chronological frameworks are typically NOT the most effective frameworks to use in most scientific communication.
Make a positive determination that time is the most important variable for an argument before selecting a chronological framework.
Lists are useful frameworks when part of a clearly-defined hierarchy.
DEFINITION: Lists use sequential indicators (e.g. numbers or letters) to connect elements to a category .
For example, the "Frameworks" category contains a list of three types of frameworks: Chronology, List, and Reasoned.
Lists are most useful in contexts where the elements of the list are part of a hierarchy, often to categorize data (Green and Lawlor, 2017). A hierarchy is a structure where elements are categorized by their importance or inclusiveness. Less inclusive elements are contained within more inclusive categories. For example, "Frameworks" is a more inclusive category than "Chronology," "List" or "Reasoned" because "Frameworks" includes all possible frameworks to structure information (whereas the other categories do not). The elements of a list share a strong connection to the more inclusive topic, but are not necessarily similar to each other.
Lists are strongest when they include three items or fewer at each level of the hierarchy. Limiting the number of elements of lists helps to avoid overwhelming audiences with information.
Lists are a useful framework as long as the presentation CLEARLY indicates the HIERARCHY that the list represents. A simple way to clearly indicate a hierarchy is to explain the more inclusive category before enumerating the items in the list.
Reasoning is the strongest framework for scientific communication.
DEFINITION: Reasoning is the process of coming to conclusions using logic (McCall, 1952).
For example, reasoning allows us to make common-sense conclusions during everyday life. If you want to know why the light in your room is off, you might reason
* Wall switches turn lights on and off
* The light is off
* Therefore, the wall switch must be in the "off" position.
You would reasonably toggle the light switch to turn the light on.
Clearly, we use reasoning all the time, and most people are quite good at reasoning (Platt, 1964; Desy, 1976; Ritchhart, 2004). However, many people are not accustomed to using reasoning as a framework to structure abstract thinking and communication (Banilower et al., 2013). Therefore, it may be useful to explore WHY reasoning is the strongest framework available for scientific communication, WHAT the elements of reasoned argument are, and HOW different types of reasoning can effectively construct arguments.
Reasoning involves making arguments
Textbooks and websites often divide communication into separate types, such as "Narrative," "Descriptive," "Analytical" or "Argumentative" (e.g. Purdue OWL). Some books present as many as 8 or even more categories of communication (Schnorenberg, 2013).
However, in a practical sense, it is very difficult to communicate without making arguments (Erduran et al., 2004). We always communicate based on a particular set of assumptions and values. Moreover, successful communication involves transferring information to someone else, which changes their thinking or perspective (Day and Gastel, 2011). Therefore, communication involves changing someone's thinking to accept new information predicated on our assumptions and values (which is a reasonable definition for making a persuasive argument; Mirriam Webster Dictionary).
For example, narrative communication is often structured around themes or objectives for readers to take from a story. Although narrative communication may employ a framework that does not seem persuasive (like a chronology), the author nonetheless selects a story, story structure, and wording to support a theme or objective. The process of selection can be seen as an implicit argument for the the theme. Similarly, although the content of descriptive essays may seem objective, the author still makes many decisions about what information to include and what information to exclude. Deciding what to include in a description is an implicit argument, often for the importance of some pieces of information over others. Because of stringent word count requirements, reporting scientific research very often involves making choices about what information to include and to exclude. Moreover, it is difficult or impossible to make purely objective word choices in many contexts, resulting in implicit arguments (Ross et al., 2017). Therefore, many types of communication involve implicit or explicit arguments with the objective of changing thinking in a desired way.
If we accept the premise that much communication (including scientific communication) is fundamentally argumentative, we can move on to the question: How can we make effective arguments?
Over 2,000 years ago, Aristotle proposed three basic strategies for making compelling arguments (i.e. "Rhetoric;" Aristotle, ~350 BCE):
ETHOS, supporting the credibility of the author,
PATHOS, appealing to the feelings or intuition of the audience, and
LOGOS, basing the argument on reason.
Scientific communication involves all three strategies: Ethos, Pathos, and Logos.
However, scientists do NOT consider arguments based on Ethos and Pathos to be strong.
Although establishing a baseline of credibility is important, scientists do NOT accept arguments based solely on the authority or status of a researcher (which is why you should critically question every recommendation in this module). Moreover, scientists also try NOT to make decisions influenced by feelings or intuition. Although feelings and intuition can be useful for creating hypotheses, intuition can lead to decisions that reflect heuristic short cuts instead of strong inference (Tversky and Kahnemann, 1974). Moreover, informal arguments that use Ethos and Pathos can also be complicated by a number of rhetorical tricks that persuade without robust evidence.
Therefore, although Ethos and Pathos ARE a part of scientific communication, their use is usually very limited and subtle. Scientific arguments cannot be based primarily on Ethos or Pathos.
Scientists construct arguments that are not only persuasive, but also truth seeking (Ramage et al., 2016). Therefore, scientists must base decisions on REASONED frameworks (Logos) to the extent possible. Using effective reasoned frameworks is essential for making scientific arguments.
Reasoning is the strongest framework for scientific arguments. Although other frameworks (e.g. lists or even chronologies) may be useful in limited areas, other frameworks typically support reasoned arguments.
Heuristics can lead to unreasonable decisions
DEFINITION: "Heuristics" are practical rules that people use to come to conclusions and make decisions (Tversky and Kahneman, 1974).
For example, consider the following description of a person: "My third cousin Steve is very shy and withdrawn. He is invariably helpful, but has little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail” (Tversky and Kahneman, 1974).
What do you think Steve most likely does for work?
A) Farmer B) Salesperson C) Librarian D) Airline Pilot E) Physician
Based on the description, most people would conclude that Steve is most likely to be a librarian.
However, there are over 100 times more salespeople in the United States than librarians (Bureau of Labor Statistics, 2018)! Clearly, Steve is much more likely to be a salesperson than a librarian. Therefore, by basing our conclusion on the resemblance of Steve to our stereotypes about different professions, we came to a conclusion that was most likely false. Our decision reflected a cognitive bias instead of a reasonable estimation.
Researchers have identified many cognitive biases that affect decision-making. Three important biases are "Representativeness," "Availability," and "Anchoring" (Tversky and Kahneman, 1974).
1) REPRESENTATIVENESS: Basing decisions on the resemblance of things to categories or stereotypes.
People's evaluation of the likely career of "My third cousin Steve" above is an example of the Representativeness Heuristic. People systematically allow stereotypes to bias their decisions.
The Representativeness Heuristic can also affect academic performance. Representativeness is one of the heuristics that students use to answer exam questions (Maeyer and Talanquer, 2010). Students often indicate that they select answers because they "seem right" instead of citing specific reasons that the answer is correct. Students who use heuristics to make decisions are unlikely to perform well on exams based on reasoned evaluation.
Representativeness can also affect other aspects of academics such as Student Evaluations of Teaching (SETs). SETs may better reflect how representative professors seem of experienced teachers than course effectiveness itself (Langbein, 1994; Uttl et al., 2017).
2) AVAILABILITY: Assessing probability based on ease of recall.
For example, people often assess risk based on easily-remembered events rather than on the actual risks involved (Tversky and Kahneman, 1974). Consider travel, where many people who frequently drive in cars take out extra insurance to fly on airplanes. Airplanes do occasionally crash, and airplane crashes are usually widely covered in the news media. However, driving in cars (in the U.S.) is far more dangerous than flying in commercial aircraft. For example, in 2015 car travel averaged 1.13 fatalities per 100 million miles driven, whereas airplanes had zero fatalities (NTHSA, 2015).
Nonetheless, many people use the availability heuristic to assign risk to events based on memorable events, instead of taking known probabilities into consideration.
3) ANCHORING: Being influenced by how information is presented.
For example, in less than 5 seconds, please give an estimate of the following products:
A) 12 * 8 * 9 * 10 * 11 * 3 * 7 * 6 * 2 * 4 * 5 = ?
B) 3 * 2 * 5 * 4 * 6 * 9 * 7 * 11 * 12 * 10 * 8 = ?
Which product (A or B) is larger?
Many would guess that (A) is larger than (B). However, on closer inspection we notice that both (A) and (B) have the same product: 479,001,600. Because of "anchoring," many people are influenced by the fact that the first numbers of (A) are larger than the first numbers of (B), and therefore estimate that the product (A) is larger.
"Anchoring" also causes people to systematically overestimate the probability of conjunctive events (events happening together or in succession) and systematically underestimate the probability of disjunctive events (events NOT happening together or in succession; Tversky and Kahneman, 1974).
For example, consider a project that involves a number of steps, each with a certain probability of success. If the first step of the project succeeds, because of "anchoring" people are more inclined to think that subsequent steps will also succeed and systematically over-estimate the overall probability of success of the project. Conversely, consider a system like a nuclear reactor with many parts (a complex system), where failure of any single part can cause the entire reactor to fail. Because each part has a low probability of failure, people tend to under-estimate the probability that the entire system will fail. In reality, the more parts that a system has, the higher probability of failure of the entire system (because the probabilities multiply).
Heuristics and other cognitive biases can influence decision-making and lead to unreasonable conclusions. Employing specific, forthright reasoning can help prevent cognitive biases from affecting judgments.
Rhetorical tricks are informal and misleading ways of making arguments
Rhetorical "tricks" are often presented as reasoning, but in reality seek to confuse arguments or to appeal to people's biases. There are many examples of rhetorical tricks. Below are a small number of examples:
1) Tricks to confuse arguments.
A) Red Herrings (distractions added to argument).
Example: Overwhelming evidence from across the natural sciences support the hypothesis that human activity is substantially changing the climate (Intergovernmental Panel on Climate Change, 2014).
Red Herring: "It’s a snowball, and that’s just from outside here, so it’s very, very cold out, very unseasonable.” -- James Inhofe, (R-OK), 2014.
* Weather is a red herring in arguments about climate because weather is a local phenomenon and climate is global.
B) Argument from ignorance (we can't prove something so it isn't happening)
Example: Overwhelming evidence from across the natural sciences support the hypothesis that human activity is substantially changing the climate (Intergovernmental Panel on Climate Change, 2014).
Argument from ignorance: "I think that measuring with precision human activity on the climate is something very challenging to do and there’s tremendous disagreement about the degree of impact. So no, I would not agree that it’s a primary contributor to the global warming that we see. But we don’t know that yet, we need to continue to debate, continue the review and analysis.” -- Scott Pruitt, 2017
* There are always things we do not know. However, being ignorant in some respects does NOT mean that we do not have enough information to be very confident that human activity is changing the climate.
C) Shifting burden of proof (if you can't prove I'm wrong, I'm right)
Example: The fact that there are Muslim terrorists does not mean that all Muslims need to prove that they are NOT terrorists (Pew Research Center, 2017).
Shifting the burden of proof: "But the silence in the face of extremism coming from the best-funded Islamic advocacy organizations and many mosques across America is absolutely deafening. It casts doubt upon the commitment to peace by adherents of the Muslim faith." -- Mike Pompeo, 2013.
* Shifting the burden of proof changes the debate from a presumption of innocence (i.e. not supporting terrorism) to a presumption of guilt.
2) Tricks to appeal to biases
D) Ad hominem (attacking the speaker)
Example: Donald Trump proposes to build a wall on the U.S. Mexico border
Ad hominem attack: "Donald Trump is morally unfit for office" -- James Comey.
* The personal character of politicians does not necessarily relate to their policy positions.
E) Appeal to authority/history
Example: The United States prints the slogan "In God we Trust" on currency, and states "one country, under God" in the pledge of allegiance.
Appeal to authority/history: "There is of course nothing in the decision reached here that is inconsistent with the fact that school children and others are officially encouraged to express love for our country by reciting historical documents such as the Declaration of Independence which contain references to the Deity or by singing officially espoused anthems which include the composer's professions of faith in a Supreme Being, or with the fact that there are many manifestations in our public life of belief in God." -- Aronow vs. United States, 1970.
* The Supreme Court argues that a historical privilege for religious statements justifies continued privilege for religious statements.
F) Appeal to the masses (everyone can't be wrong)
Example:Widespread opposition to the Vietnam war.
Appeal to the masses: "And so tonight—to you, the great silent majority of my fellow Americans—I ask for your support." -- Richard Nixon, 1969.
* Politicians frequently argue that mass support for them justifies their policies.
There are many rhetorical tricks. Learning about rhetorical strategies is interesting, and can be helpful for identifying weak arguments.
Three basic elements make up reasoned arguments
Reasoning and logic can seem (and be) extremely complex. However, logic can be broken down into small sets of elements. Therefore, it is useful to review some fundamental elements of reasoned arguments:
1. CONCLUSIONS: The outcome of arguments.
2. PREMISES: Statements that contain information for an argument.
3. TRANSITIONS: Ways to logically connect premises.
Reasoned arguments involve coming to conclusions based on a set of premises connected by logical transitions. Practically, deliberately using a reasoned framework can simplify writing because almost every sentence is clearly one of three types: conclusion, premise, or transition.
To understand how to construct compelling arguments, we will first need to define and explain each component:
Conclusions are WHY we present reasoned arguments.
"Conclusion" has two common meanings. First, a conclusion is the outcome of an argument (which could involve judgment). Second, a conclusion often refers to finality, or ending. The two meanings of "conclusion" are consistent with each other because the outcome typically comes at the end of an argument.
What makes a strong conclusion?
"We reject our measurable hypothesis that students who drink coffee will have significantly greater sprint performance than students who do not drink coffee."
Conclusions can motivate and initiate arguments.
In addition to clearly stating the outcome of an argument, conclusions have an additional role. Having evidence sufficient to support a conclusion is typically what motivates the process of presenting a scientific argument. When scientists are confident that their evidence supports a conclusion, the scientists can begin the process of presenting their research. Therefore, scientific conclusions often perform the function that "claims" serve in more general argumentation frameworks (Toulmin, 1958). Arriving at a reasonable conclusion allows scientists to make a "claim," and begin of the process of constructing written arguments that communicate scientific findings.
Some educational frameworks directly apply the terminology of "claims" to scientific contexts (McNeill, 2008). However, the word "claim" connotes statements that can be made without evidence (e.g. Oxford Living Dictionary). In contrast, conclusions require supporting evidence. "Claims" that remain tentative are commonly termed scientific "hypotheses." However, even hypotheses are typically derived from extensive theoretical or empirical evidence. Therefore, scientists do not commonly use the word "claim" to refer either to hypotheses or conclusions.
Clearly-stated conclusions can both motivate and culminate scientific arguments. Therefore, using conclusions as both topic sentences (at the beginning of arguments) and outcomes (at the end of arguments) can help structure understandable and persuasive reasoning.
Strong reasoned arguments begin and end with strong, clear conclusions. Determining the conclusion (the goal) of an argument before writing can greatly help to develop strong arguments.
Premises are the units of information for reasoned arguments.
DEFINITION: In the context of empirical science, a premise (or "proposition") is a unit of information in an argument (Layman, 2005).
Practically, a premise is often expressed as a sentence that conveys ONE piece of information. Sometimes, additional sentences clarify the main statement of a premise.
What is a unit of "information?" The two primary types of information that scientists use as premises to structure arguments are ASSUMPTIONS and FACTS.
Clarifications can support both assumptions and facts. Clarifications are not strictly necessary for strong reasoned arguments, and therefore are not as important as facts (or assumptions). However, clarifications may be helpful for explaining premises to the audience.
Assumptions are statements that are NOT supported by evidence. Use assumptions as premises only when NECESSARY. Identify and communicate assumptions you must make.
DEFINITION: Assumptions, or "beliefs," are statements that are not directly supported by empirical evidence.
For example, there are Three Main Assumptions of Science (TMAS):
1) The universe is real.
2) Humans can accurately perceive the universe.
3) Natural processes are sufficient to understand the universe
We do NOT have direct evidence for the TMAS! Debating assumptions such as "The world is real" may be appropriate for philosophy. However, instead of debate, scientists typically accept the TMAS and move on.
Scientists seek to understand the universe while making as FEW assumptions as possible
A main goal of science is to create better and better models of the world that are based on as few assumptions as possible. Each assumption in a scientific model is a potential source of error, because assumptions can easily be incorrect. Ideally, scientists would be able to conduct experiments and build models using only the TMAS.
However, in practice, scientists are often forced to make additional assumptions. For example, a researcher may only have access to 5 males and 15 females in Group A, and 11 females and 9 males in Group B. The researcher may assume that males and females have comparable physiology for the specific question that the researcher is asking. Based on the assumption that physiology is the same for males and females, the researcher may compare outcomes of Group A to Group B despite the sex differences between the groups. Evidence from other studies can contribute to the conclusion that an assumption is reasonable.
Scientists inevitably make some assumptions when conducting research. Most experimental research cannot reasonably avoid (test) EVERY assumption required to perform an experiment. Moreover, scientists (like all humans) make assumptions that they are not even aware of (Gould, 1981). Therefore, purely descriptive or objective communication is simply not possible. The best that scientists can practically do is to identify the assumptions that they make and to honestly communicate all potentially important assumptions to their audience.
Make as few assumptions as possible. Clearly identify all assumptions that must be made, and explain the reasons why each assumption is necessary.
Statements of fact are the primary building blocks of reasoned arguments.
Premises can be simple statements of "fact." For example: "The Earth circles the Sun," or "The United States is on the Earth."
You could make the argument:
PREMISE: The Earth circles the Sun
PREMISE: The United States is on the Earth
CONCLUSION: Therefore, the United States also circles the Sun.
Premises can also be more complex or conditional statements. For example "If the sun comes out, the snow will melt." Factual information is the primary source of evidence for scientific arguments (e.g. McNeill, 2006).
However, premises are NOT necessarily true! "The Sun circles the Earth" is a statement that people who lived before Copernicus agreed with (and is consistent with visual observations). The statement "The Sun circles the Earth" is a reasonable premise for an argument, even though we now know that the statement is incorrect. Therefore, for our purposes a "premise" refers to a piece of information that IS part of a reasoned argument, but does NOT necessarily imply that the information is true.
Although premises do not need to be true to construct arguments, scientific arguments will only be strong if based on true premises.
When are premises most likely to be true?
Ultimately, scientific truth is based on repeated, objective measurements. "Objective" measurements are where the measurements themselves do not depend on human judgment or interpretation (therefore qualitative research, case studies, professional opinions, etc. do not provide strong premises for science; Ebell et al., 2004). Scientific research separates data collection (which strives to be quantitative and objective) and data interpretation (which is "qualitative:" depending on reason and judgment).
Repeatability means that measurements are "reliable": able to be measured by any researchers who make the measurements appropriately.
There are three primary sources of repeated, objective measurements in science:
1) Premises based on data that you collect YOURSELF.
2) Premises based on data that OTHER people have reported and subjected to rigorous peer-review.
3) Premises that are the CONCLUSIONS of valid or strong arguments (i.e. well-reasoned arguments based on true premises):
Objective data that you collect can be premises.
Science is empirical: ultimately based on measurements of the observable world. Objective measurements (data) that you collect yourself, and calculations based on your measurements, can be used as premises for reasoned arguments. For example, if you collect data on sprint performance from two groups of athletes that differ in caffeine consumption, you could construct the argument:
PREMISE: Our measurable (null) hypothesis is that sprint performance will not be significantly different between the caffeine-drinking group and the caffeine-free group. We used sprint speed to measure sprint performance.
(N.B. a hypothesis can be a premise if the hypothesis is the conclusion of a strong reasoned argument based on peer-reviewed, objective research. For simplicity, the argument leading to the hypothesis is not presented here.)
PREMISE: Sprint speed in the caffeine-drinking group was 11% higher than in the caffeine-free group (t-test, P<0.001; Figure 1).
CONCLUSION: Therefore, we reject our measurable hypothesis that there is no significant difference in sprint performance between the caffeine-drinking and caffeine-free groups.
For measurements to be strong premises, the measurements must be collected in a scientifically rigorous way. Scientific rigor involves many practices that are outside the scope of the present discussion. However, some important aspects of rigorous data collection are:
1) Objective, quantitative, reliable, and valid measurement techniques.
2) Appropriate controls and normalization.
3) Appropriate data analysis and statistical tests.
Place references in parentheses at the END of sentences.
For hypotheses-driven research, raw data by themselves are not the most important aspect of any section of a manuscript. The role of raw data is to create premises to support reasoned arguments. Because of the supportive role of data, place references to figures, tables etc. at the END of sentences. For example, the sentence "Sprint speed in the caffeine-drinking group was 11% higher than in the caffeine-free group (t-test, P<0.001; Figure 1)" references the the outcome of the statistical test and the figure at the end of the sentence.
Objective, reliable, and valid data that you collect in a rigorous manner can be used as premises for reasoned arguments. To use data as a premise, you must refer directly to specific data. Place references to figures and tables at the END of sentences.
Data collected by others can be premises.
One of the criteria for scientific rigor in data collection is that methods be reliable (making the same measurement of the same phenomenon yields the same result; Carmines and Zeller, 1979). For reliable measurements, who collects the data does not change the data themselves. Therefore, data rigorously collected by other researchers can also be used as premises for reasoned arguments. For example:
PREMISE: Our measurements of sprinting speed supports the hypothesis that caffeine increases sprint performance.
PREMISE: Moderate doses of caffeine can also increase performance during endurance tasks (Southward et al., 2018).
CONCLUSION: Therefore, data from sprint and endurance performance both support the general hypothesis that moderate doses of caffeine increase motor performance.
The second premise in the argument references a conclusion reasonably derived from data published in a previous study (Southward et al., 2018).
Published scientific studies differ in quality and the strength of their conclusions (Ebell et al., 2004). Researchers invest years in training and work experience to be able to critically evaluate the quality of scientific publications. However, some basic criteria for determining whether a published study is acceptable for supporting a premise are
1) Your premise accurately reflects the data or conclusions of the study.
2) The study you cite is quantitative (not qualitative, testimonial, editorial, etc.).
3) The publication you cite has undergone a rigorous peer-review process.
Scientific publications do NOT commonly include direct quotes from other papers. Because you are constructing a premise (using previously-published data) that must clearly fit into your argument, direct quotes are seldom appropriate. Instead, explain (paraphrase) the previously-measured data in a way that fits within your argument.
The type and format of citations differ among publications. Simple formats such as (Author, date) are sufficient for contexts where word limits are not a constraint.
Scientific publications use a wide variety of format, fonts, and citation styles. Different journals use a wide range of page layouts, margins, indents, and every other aspect of presenting publications. For example, whereas "APA" format may be used within psychology, APA style is not used in many other fields.
However, scientific publications do commonly use in-text citations and bibliographies at the end of papers, and do not commonly use footnotes. In-text citation styles differ among publications: some publications use (Author, date), others use numbers (e.g. in brackets:  or superscripts1). Bibliography format differs even more among journals, books, and online databases. Therefore, there is no standard for formatting scientific communication and references.
In-text citations and bibliographies have one primary purpose: to allow you to find the referenced information (e.g. in the library or in an online database). Scientific communication is indexed (organized) by author name. Therefore, author name is usually an important part of finding publications.
However, organizing information based on author name does not mean that the authors of a study themselves are particularly important. Again, if data are valid and reliable, then it doesn't matter who collected and analyzed the data. It is the data and the conclusions from the data, not the author, that we use as a premise. Therefore, do not make authors the subject of sentences in scientific publications. Instead:
Place references to past research in parentheses at the END of sentences.
For example, do NOT write sentences such as:
"Jindrich and Full (2002) found that intrinsic muscle properties contribute to locomotor stability."
Instead, write sentences that focus on the data or conclusions of past studies, with references at the END of the sentence:
"Intrinsic muscle properties contribute to locomotor stability (Jindrich and Full, 2002)."
Premises based on data from other researchers are important aspects of most scientific publications. To be strong premises, information should be quantitative, peer-reviewed, and support an argument. Place all references at the END of sentences.
Reasoned frameworks are powerful because reasoning allows for ABSTRACTION. If arguments lead to strong conclusions, then the conclusions can be used as premises.
DEFINITION: "Abstraction" is the ability to focus on the the most important characteristics of an object or a concept without having to consider its details or connections to other objects/concepts (Locke, 1689).
We use abstraction all the time in daily life. For example, we use computers without knowing how most of the internal components work. Abstraction allows us to consider the computer as a simple-to-use tool and not think about the millions of circuits in every computer. Abstraction also helps manufacturers make computers in the first place. For example, many types of computer screens may share a common type of plug. The screen does not "know" what kind of computer it is connected to. As long as the screen receives appropriate inputs from the computer, the screen can properly display information. Therefore, abstraction is an essential part of modern life.
How can we use abstraction to improve our reasoning and writing?
We can we use abstraction to improve our reasoning and writing by constructing reasoned arguments that are MODULAR, where each argument is self-contained. Self-contained (modular) arguments have two important properties:
1) The argument supports ONE clearly-stated conclusion.
2) All of the information required to support the conclusion is contained in the argument.
Modularity and abstraction can help us construct more complex arguments from simpler arguments. For example, consider an argument that soda consumption is associated with obesity in children. We might imagine our overall argument to be:
PREMISE: In the past 50 years, obesity rates have increased concurrently (at the same time) as increases in soda consumption (i.e. there is a correlation between obesity and soda consumption).
PREMISE: Plausible physiological mechanisms link soda consumption and obesity .
CONCLUSION: Therefore, both correlations and physiological evidence suggest that soda consumption may cause obesity in children.
What is a problem with the premises of the argument? One problem is that the premises do not include citations, either to specific data or to peer-reviewed, quantitative research studies. Perhaps studies correlating obesity to soda consumption and investigating plausible mechanisms simply do not exist. However, even if studies to support each premise do exist, another important problem remains.
PROBLEM: General scientific questions are almost never answered by a single research study!
Most significant scientific questions require many experimental and theoretical studies to address. Therefore, using a single reference, or even several references, would not provide enough context and evidence to adequately support the premise. Each premise may require the support of a separate, modular, reasoned argument.
To support the first premise, we could construct a simple argument:
MODULAR ARGUMENT 1:
PREMISE: From 1970 to 1990 there was a 123% increase in soda consumption among children (Hu and Malik, 2010).
PREMISE: From 1986 to 2006, the percentage of obese children doubled (Hedley et al., 2004).
CONCLUSION 1: Therefore, in the past 50 years, obesity rates have increased concurrently (at the same time) as increases in soda consumption.
Arguments do not need to only have two premises. For example to support the second premise of our general argument, we could construct another modular sub-argument:
MODULAR ARGUMENT 2:PREMISE: Sugar consumption increases calorie intake (Ludwig et al., 2001).
PREMISE: Sugar consumption also changes metabolism to favor fat storage (Brand-Miller et al., 2002).
PREMISE: Consuming sugar as liquid is less satisfying, increasing calorie intake (DiMeglio and Mattes, 2000).
PREMISE: Soda consumption displaces milk consumption among children, reducing the obesity-preventing role of calcium (Miller et al., 2001).
CONCLUSION 2: Therefore, there are plausible physiological mechanisms that link soda consumption and obesity.
If both modular arguments are strong (or sound), then we can consider both conclusions to be reasonable and well-supported (or "true"). Reasonable, well-supported conclusions can be used as premises for arguments. Therefore, modular arguments and the process of abstraction can help simplify reasoning by allowing complex arguments to be broken down into simpler elements. Modular arguments also leverage the power of hierarchies to structure and communicate complex ideas.
We do not have either our data or a single source that can appropriately defend many premises. Using the principle of abstraction, we can construct modular arguments to support clear conclusions. If the modular arguments are strong or sound, then the conclusions can be considered "true" and used as premises for a larger argument.
Premises can sometimes benefit from additional support. Clarifications provide additional information that help readers understand premises.
Science is a human process, and the impact of scientific research depends on people being able to clearly understand the questions and results of scientific studies. Therefore, the more people that can understand a research study, the better chance that the study will have an impact within and outside of science.
Limiting arguments to premises, logical transitions, and conclusions would be acceptable from a logical standpoint. However, many readers may have difficulty following arguments that involve only the most important elements of logic (premises and conclusions). Therefore, although not as important as premises or conclusions, clarifications can help to support arguments by explaining and contextualizing the elements of reasoning.
Three main clarifications that can contribute to clarifying premises and arguments are DEFINITIONS, EXAMPLES, and SUMMARIES.
1. DEFINITIONS. Definitions are useful for clarifying terminology or concepts. Definitions are most useful if they appear immediately before or after the term being defined. Definitions can be expressed in terms of concepts or sometimes in terms of procedures ("operational" definitions). Importantly, definitions are most useful if they use terminology that is substantially simpler (more common) than the term being defined. In scientific writing, definitions usually require references.
Common terms for definitions:
XX is defined as...
XX can be considered....
2. EXAMPLES. Examples provide specific instances that clarify a premise. Examples are most useful if they appear immediately after the concept being illustrated. Examples are most useful if they are short (usually 1-3 sentences). In scientific writing, examples usually require references.
Common terms for examples:
3. SUMMARIES. Summaries provide a concise explanation of the main logical progression of complex arguments. Summaries are most useful if they do not simply re-state the conclusions of an argument, but explain the most important logical steps leading to a conclusion. Summaries are typically placed immediately after the conclusion of one or several reasoned arguments.
Common terms for summaries:
On the whole
However, some commonly-used phrases are NOT clarifications.
Some words and phrases may appear to be clarifications, but actually detract from arguments instead of supporting them. For example, the phrase "in other words" may seem helpful, but really should not be necessary. Writing or saying "In other words" is little more than an admission that the words used the first time were unacceptable in some way. There should be no need to re-state premises or conclusions if the arguments were clear the first time.
Clarifications can help to support reasoned arguments. However, clarifications are NOT the most important elements of arguments (i.e. premises and conclusions). Therefore, include clarifications only AFTER constructing a strong or sound argument from premises leading to a conclusion.
Logical Transitions connect premises together to form reasoned arguments.
Modular arguments support a single conclusion, but may contain MANY premises. The premises of an argument must be connected together to reasonably lead to a conclusion. How can premises be connected together?
Using straightforward "Logical Transitions" to connect premises together and to indicate conclusions can simplify and clarify scientific writing.
DEFINITION: "Logical Transitions" are words that indicate the logical relationships between separate elements of an argument (e.g. premises).
Among the many written transitions available to writers, three basic Logical Transitions commonly relate premises:
1. CONJUNCTIONS: Connecting premises that may both be true together into specific relationships.
2. DISJUNCTIONS: Separating premises into exclusive categories (both premises cannot be true).
3. CONCLUSIONS: Indicating the logical outcome of the premises.
The following sections review the basic logical transitions, and some of the many English words that can indicate each type of transition.
Conjunctions connect premises together in several different types of relationships.
Conjunctions connect two premises that can BOTH simultaneously be TRUE.
The simplest conjunction is "and." For example,
PREMISE: All humans breathe AND
PREMISE: I am a human
CONCLUSION: Therefore, I breathe.
The conjunction AND links the two premises together to form the argument. Both premises can be (and hopefully are) true at the same time. The conclusion naturally follows if both premises are true.
HOWEVER, "and" is not the only conjunction! Premises can have many relationships other than simply being true at the same time. For example, two premises can both be true, but contrast or conflict in some way.
The conjunction "but" expresses contrast or conflict. For example,
PREMISE: Electronic cigarettes do not have all of the toxins that tobacco contains BUT
PREMISE: Electronic cigarettes contain other toxins like formaldehyde
CONCLUSION: Therefore, both tobacco and electronic cigarettes can be harmful to health.
Although "and" and "but" are the simplest and most common conjunctions, there are more than one way of expressing each type. Moreover, hierarchical conjunctions can help to express the relationships among information. The following links discuss each type of conjunction in more detail.
AND transitions are perhaps the most common and least powerful logical transitions.
"And" transitions are possibly the most common logical transitions. One reason that AND transitions are common is that "and" is often implicit and assumed when a transition is not plainly identified.
For example, the sentences "All mammals have hair. Most mammals give birth to live young." are connected with an implicit "and" transition. The sentences could easily be combined to read "All mammals have hair, and most mammals give birth to live young."
Therefore, for many statements that use the "and" transition, the word "and" is implicit and unstated.
"And" transitions are also common because "and" can connect elements of LISTS and CHRONOLOGIES. Therefore, when lists or chronologies are used to structure parts of a document, "and" transitions commonly connect the elements of the list or chronology. For example, we could visualize our list of frameworks as connected by "and" transitions
"And" transitions are ubiquitous and useful because they are the simplest way to connect elements of information that might both be true. Many premises can be connected using "and" transitions.
The word "And" is not the only way to reflect the AND conjunction. Some other words that can also express "And" are:
First, second, etc.
Finally, Last, etc.
However, the simplicity and versatility of the "and" transition is also a major drawback. "And" transitions by themselves are not SPECIFIC. Connecting two pieces of information with an "and" transition only tells the audience that the two pieces of information may both be true, but does NOT indicate anything else about the relationship between the two pieces of information.
Sometimes, it is possible to use logical transitions that are similar to "and," but more specific. For example, if two premises are similar to each other, then expressing the similarity is more powerful than simply declaring both premises. Similarity can be expressed using several terms, such as:
In the same way
Just as... so too
A similar x
Similarity transitions are more "powerful" than "and" transitions because similarity transitions convey more information than simply "and."
The "And" logical transition is common and easy to use. Although the "And" transition is appropriate in many situations, it is important to make sure that more powerful transitions (e.g. similarity, hierarchical conjunctions, "but") are not appropriate before using "And."
Hierarchical conjunctions can provide valuable information about the relationships among elements.
DEFINITION: Hierarchies organize information according to importance or inclusiveness.
For example, the Linnaean taxonomy is a hierarchical organization based on inclusiveness: more inclusive taxa (Kingdom, phylum, etc.) are at a higher level of the hierarchy than less inclusive taxa (Genus, Species).
Connecting elements of information hierarchically provides more specific information than simply using an "and" conjunction.
For example, we could write "The order Coleoptera (Beetles) is the largest order in the entire animal kingdom. Within the order Coleoptera are over 350,000 species of beetles." The word "within" serves as a conjunction that indicates that the category species is a subset of the larger category Coleoptera.
Hierarchical conjunctions can be important ways of connecting elements of arguments within a specific context. Because identifying hierarchical connections provides context, hierarchical transitions are more powerful than "and" conjunctions.
There are many ways to indicate hierarchies. However, two main categories are subsets (less inclusive groups within a larger group) and supersets (more inclusive groups that contain a smaller group).
EXAMPLES OF HIERARCHICAL CONJUNCTIONS
X is part of the larger category of...
A member of X is...
X belongs to...
Elements of X are....
X is one element of...
X is contained within...
X can be divided into...
A more inclusive category is...
Establishing and identifying hierarchies among concepts can be an extremely powerful way to organize thinking and writing. Using hierarchical conjunctions among elements of an argument can help establish context and make writing stronger and more explanatory.
"But" transitions are conjunctions that express contrast. "But" transitions can greatly help clarify differences and maintain reader interest.
"But" transitions are conjunctions because both premises linked by a "but" transition can simultaneously be true. However, "but" transitions indicate that two premises contrast or conflict in some way. For example, we could write "Both insects and mammals have muscles, but insects have inhibitory motor neurons (Wolf, 2014)." Even without clarification, the "but" transition implies that mammals do not have inhibitory motor neurons (which we do not).
"But" transitions are stronger than "and" transitions for at least three reasons:
1) "But" transitions express more information than "and" transitions. By expressing contrast or conflict, "but" transitions not only indicate conjunction (as "and" transitions do), but also indicate the relationship of the premises to each other. The author must specify why the two premises contrast if the reason is not self-evident.
2) "But" transitions are more interesting than "and" transitions. Conflict and change is fundamental to human storytelling (Campbell, 1991). However, conflict is not only interesting in the context of fiction writing (Lebrun, 2011). Emphasizing conflict can also make non-fiction communication (including scientific writing) more interesting for an audience (Olson, 2015; Luna, 2013).
3) By expressing contrast, "but" transitions may facilitate understanding and retention of information. Contrast is potentially an important facilitator of constructing long-term memories (Bilodeau, 1966). Therefore, expressing contrast may help audiences retain information.
Therefore, emphasizing conflict and contrast can be very helpful for constructing interesting, compelling, and memorable arguments.
Fortunately, the word "but" is not the only way to express a "but" transition.
More "but" Transitions
On the contrary
On the other hand
At the same time
Clearly, most arguments are not constructed solely from conflicting information. In practice, some combination of "and" and "but" conjunctions is often the most compelling way to structure a series of arguments.
Expressing contrast and conflict through "but" conjunctions can make arguments more interesting and memorable. Looking out for sources of potential conflict can contribute to compelling communication.
Disjunctions are helpful for creating analytical arguments.
DEFINITION: (exclusive) disjunctions identify elements that CANNOT be true at the same time.
(N.B. In logic, the specific term for exclusive disjunctions is "exclusive or," or "xor." Philosophers and computer scientists typically use the word "or" to refer to "inclusive or," meaning "either... or (or both)" can be true (Layman, 2005). "Disjunctions" in a broad sense includes both inclusive and exclusive "or." However for simplicity I will use the common, vernacular sense of the word "or": referring to exclusive disjunctions instead of the definition of "or" as inclusive disjunctions used in computer science).
For example, we might state "Either the coin will land on heads or the coin will land on tails." When there are two mutually-exclusive outcomes (like a coin flip), we can term the exclusive disjunction a "dichotomy."
Dichotomies are extremely useful for science. Dichotomies can help us analyze problems (i.e. break problems down into their constituent parts).
For example, if we can define a problem such that an experiment has only two possible outcomes, one of which rejects Hypothesis A and one of which rejects Hypothesis B, then we can be sure of an important conclusion regardless of the results of our experiment.
A disjunctive argument could be:
PREMISE: Either DNA or protein is the heritable genetic material that determines phenotype.
PREMISE: Proteins do NOT heritably determine phenotype (Hershey and Chase, 1952).
CONCLUSION: Therefore, DNA is the heritable genetic material that determines phenotype.
TRUE dichotomies are EXTREMELY USEFUL (Platt, 1964). However, true dichotomies can also be challenging to identify. Moreover, it is important to be cautious of FALSE dichotomies, a common logical fallacy.
For example, the dichotomy "Either exercise prolongs life or it doesn't" is a false dichotomy because it does not address several legitimate alternatives. Specifically, the statement doesn't specify how much exercise it refers to. Whereas moderate exercise has health benefits, excessive exercise can be fatal (Knechtle and Nikolaidis, 2018).
Therefore, when using exclusive disjunctions, it is important to have strong evidence that all the alternatives are mutually exclusive, and that intermediate possibilities do not exist.
Disjunctions involve the logical transition "or." In many cases, the clearest way to indicate disjunction is simply to use the word "or" or the couple "Either...or." However, other words such as "conversely," or "alternatively" can also indicate disjunction.
Disjunctions are not only useful for reasoning. Disjunctions involve contrasts that are even stronger than "but" conjunctions. Therefore, disjunctions have from the same potential benefits (of keeping the audience interested and facilitating memory) that "but" conjunctions do.
Disjunctions, including dichotomies, are EXTREMELY USEFUL, both for analytical reasoning and writing. Discovering TRUE disjunctions is worth considerable effort. Disjunctions involve the "or" logical transition.
Several words can indicate the conclusions of reasoned arguments.
CONCLUSIONS are the most important parts of reasoned arguments (i.e. see the CONCLUSIONS section).
There are several Logical Transitions that indicate conclusions, including:
As a result
To this end
Although there are several words that indicate conclusions, it is NOT necessary to use different terms to conclude each reasoned argument that you make. Avoiding repetition is far less important than clarity in scientific writing. Therefore, it is acceptable to use the same word to indicate the conclusion of many or all of your arguments.
Two main processes allow conclusions to be drawn from sets of premises: inductive and deductive reasoning. Hierarchies of modular arguments can link simpler and more complex arguments.
Reasoning involves the process of inference.
DEFINITION: Inference is judging conclusions to be true based on one or more premises (Barker, 1989).
However, how do we know if we correctly come to conclusions based on the available premises?
The field of Logic studies how to make valid inferences.
Throughout history, concepts of logic have evolved and changed (Thompson, 1992). However, one common categorization splits logic into two main fields. First, "Formal" logic studies the structures ("forms") of valid and invalid arguments. Formal logic can be abstract, and use mathematical symbols and specific operations. Second, "Informal" logic, or "the grammar of argumentation," studies the practical aspects of using language to persuade others (Thompson, 1992).
How do scientists use logic to make inferences? Scientists use two primary modes of inference to support conclusions, DEDUCTIVE and INDUCTIVE reasoning (Moore and Parker, 2017). HIERARCHICAL structures can connect the conclusions of modular inductive or deductive arguments to build larger arguments.
Deduction can lead to conclusions or predictions based on existing knowledge.
DEFINITION: Deductive reasoning can construct arguments that are "truth preserving" (Layman, 2005; Giere, 2006). Truth preserving means that the truth of conclusions is contained within the sphere of true premises. Therefore, if the sphere of the premises includes the conclusion, and the premises are true, then the conclusion will also be true.
Deductive reasoning is useful for making predictions based on information that we already know. For example, we could make the simple argument:
PREMISE: The sun always comes up in the morning
CONCLUSION: Therefore, tomorrow morning the sun will come up.
Because we know that the sun always comes in the morning (i.e. it's a "fact"), we can conclude that the sun will come up tomorrow. The sphere of the premises (all mornings) includes the conclusion (tomorrow morning). Therefore, if it is true that the sun always comes up in the morning, then the truth will be "preserved" and apply to tomorrow morning.
Formal logic and mathematics both use deductive reasoning. Similar to mathematical proofs, deductive reasoning can establish the truth of conclusions based on appropriate and true premises. For example,
PREMISE: All mammals have hair
PREMISE: No lizards have hair
CONCLUSION: Therefore, no lizards are mammals.
The premises of the preceding argument are termed "categorical" premises because they apply generally to large categories of things (e.g. mammals and lizards, respectively). Consider also the following argument:
PREMISE: No mammals lay eggs
PREMISE: Echidnas lay eggs
CONCLUSION: Therefore, echidnas are not mammals.
Does the second argument seem reasonable?
Based on the facts (premises) presented to you, it is reasonable to conclude that echidnas are not mammals, because echidnas lay eggs and mammals don't. The argument is a valid example of deductive reasoning because the conclusion follows reasonably from premises that are demonstrably true or false.
HOWEVER, premises are not always true! Echidnas do lay eggs, but they also ARE mammals (Nicol and Andersen, 2007). The first premise of the argument is false: some mammals actually DO lay eggs. Therefore, the conclusion of the argument is also false.
Some useful terminology can help to understand deductive arguments.
DEFINITION: A "valid" deductive argument is a deductive argument where IF the premises are true, then the conclusion is also true (Cavender and Kahane, 2018).
Validity depends on the structure of the deductive reasoning. If a deductive argument is structured in a valid way, then true premises will lead to true conclusions. Valid arguments are therefore "truth preserving."
DEFINITION: A "sound" deductive argument is a valid argument with true premises (Cleave, 2016).
Soundness depends on the content and the structure of deductive reasoning. To be sound, an argument must be well-structured and also must have true premises.
Therefore, the argument about echidnas WAS valid because the conclusion would reasonably follow from true premises. However, the argument was NOT sound because not all of the premises were true.
Deductive reasoning is critical for science (Popper, 1934). Specifically, SYLLOGISMS represent the simplest deductive arguments. Using GRAPHICAL FRAMEWORKS can help to put deductive reasoning into practice. However, when constructing arguments, we must be sure to avoid using deductive FALLACIES.
"Syllogisms" are useful building blocks for deductive arguments.
DEFINITION: A "Syllogism" is a deductive argument with two premises leading to a conclusion (McCall, 1952).
For example, a famous syllogism from Aristotle:
PREMISE: All men are mortal.
PREMISE: Socrates is a man.
CONCLUSION: Therefore, Socrates is mortal.
Even arguments that may seem simpler than syllogisms can often be expressed more clearly as syllogisms. Our example, "The sun always comes up in the morning. Therefore, the sun will come up tomorrow," actually skips one step in the reasoning. The full argument is a syllogism:
PREMISE: The sun always comes up in the morning.
PREMISE: Tomorrow there will be a morning.
CONCLUSION: Therefore, the sun will come up tomorrow.
Syllogisms can also involve somewhat more complex premises like conditional (if...then) statements. For example:
PREMISE: If I go to college, then it's likely that I will get a high-paying job.
PREMISE: I am going to college.
CONCLUSION: Therefore, it's likely that I will get a high-paying job.
Some syllogisms have specific names. The last syllogism about going to college is of a specific form called "modus ponens" (Layman, 2005). Modus ponens has the general form:
PREMISE: If A then B.
PREMISE: A is true.
CONCLUSION: Therefore, B is also true.
In the previous syllogism, "A" signifies "if I go to college" and "B" signifies "it's likely that I will get a high-paying job."
Clearly, modus ponens is useful for making predictions. For example, consider that A is some cause and B is some outcome. If we know that the cause is present, then we can successfully predict the outcome.
However, one problem for research scientists is that we often don't know if A is truly the cause of something or not! If we aren't sure if the first premise of modus ponens is true, then modus ponens won't help us much.
(Or, to express the previous statements in the form of modus ponens:
PREMISE: If we aren't sure if the first premise of modus ponens is true, then modus ponens won't help us much.
PREMISE: Research scientists often aren't sure of the first premise of modus ponens.
CONCLUSION: Therefore, modus ponens won't help us much).
Arguably a more important syllogism for science is called "modus tollens" (Layman, 2005). Modus tollens takes the form:
PREMISE: If A then B.
PREMISE: B is NOT true.
CONCLUSION: Therefore, A is also NOT true.
Modus tollens is important because it allows us to soundly REJECT hypotheses (Popper, 1962). For example, if we hypothesize that green M&Ms make you smarter, we could conduct an experiment:
PREMISE: We hypothesize that everyone who eats green M&Ms gets smarter.
PREMISE: I ate green M&Ms but I am NOT smarter.
CONCLUSION: Therefore, we REJECT the hypothesis that everyone who eats green M&Ms gets smarter.
Modus tollens allows us to use data to reject hypotheses. If we can reject the hypothesis that green M&Ms make everyone who eats them smarter, then we've learned something. Perhaps green M&Ms have some other influence (or none at all).
Valid syllogisms are simple and modular. Simple and modular arguments can help us construct more complex arguments. Both modus ponens and modus tollens are valid syllogisms if expressed correctly. Therefore syllogisms such as modus ponens and modus tollens can help construct more complex arguments from simple, modular components.
Syllogisms are useful frameworks for constructing deductive arguments. The specific form of modus tollens is critical for scientific reasoning because it allows scientists to reject hypotheses.
Graphical representations can help to simplify complex arguments.
Using strong frameworks is the most important part of clearly structuring communication. Strong frameworks involve straightforward reasoning, clear premises and conclusions, clear distinctions between premises and conclusions, and judicious use of clarifiers.
However, even with strong frameworks, constructing reasoned arguments can be complicated. For example, arguments may involve many premises. Alternatively, we may wish to present several related arguments (with several premises and conclusions) in the same overall discussion. Therefore, communicating arguments can involve substantial organizational challenges.
Structure is one key to clarity despite complexity.
Graphical representations (diagrams, tables, etc.) can help strengthen the structure and clarity of arguments. The old adage "a picture is worth a thousand words" is true even for scientific communication. Graphical representation can be simple. For example, expressing the measurable predictions of general hypotheses in a table format can clarify the criteria for rejecting or supporting hypotheses.
Consider a specific (somewhat complicated) example. Imagine that we are interested in how best to organize a series of 1-hour long study sessions before a math test. Imagine that the test covers three types of problems: algebra, geometry, and word problems. Imagine also that we are interested in comparing three types of study strategies:
1) "Blocked" study, where a person studies a single topic before moving on to another topic. For example, in Blocked study, each person would spend an hour studying algebra, then an hour studying geometry, and then an hour studying word problems.
2) "Serial" study, where a person studies all three topics together in a determined order. For example, in serial study, each person could spend 10 minutes studying algebra, then 10 minutes studying geometry, then ten minutes studying word problems. The person would then repeat the process for one hour of studying.
3) "Random" study, where a person studies topics in 10-minute blocks, but the order of presentation is random. Therefore, the sequence could be: geometry, algebra, word problems algebra, word problems, geometry... etc.
We can illustrate the three types of study strategies using a table (Table 1; Lee and McGill, 1983):
Table 1. A "graphical framework" for illustrating different ways to schedule study.
Which type of study strategy (blocked, serial, or random) do you think will be most effective? Why?
An additional important question is: what does "effective" even mean? "Effective" is a fairly general term and could mean different things. Among the possible ways to measure effectiveness are:
1) Effectiveness during practice. We could potentially determine how effective each study strategy is by measuring how much people improve during the study session itself. For example, we could compare test performance at the beginning and end of each hour-long study session.
2) Effectiveness for retention. We could potentially determine how effective each study strategy was by testing for how well people retain the math skills that they studied after a period of time. For example, we could give people tests on the math problems one day, or 10 days after finishing their study sessions and measure how much of information/skill the people retained.
3) Effectiveness for transfer. Another way to measure the effectiveness of different study strategies would be to determine if people are able to transfer skills learned during practice to different types of problems. We could test people's performance on a different type of math problem such as applied engineering problems that were NOT included in the types of problems that the people studied. If studying using a particular strategy helps people solve new types of math problems, then we could consider the study strategy effective.
Is the example getting complicated? We have three types of math problems, three types of study strategies, and three ways to assess "effectiveness." For most people, 9 values in three different categories is a lot to think about at one time.
Imagine for simplicity that (as most people do), we hypothesized that a "blocked" practice schedule was the most effective practice schedule in all three respects: during studying, for retention, and for transfer. We could create measurable hypotheses for all 14 specific predictions that result from our hypothesis. For example:
1) We hypothesize that blocked study will result in significantly higher scores on algebra tests than serial study during practice.
2) We hypothesize that blocked study will result in significantly higher scores on algebra tests than serial study during the retention test.
3) We hypothesize that blocked study will result in significantly higher scores on algebra tests than serial study during the transfer test.
4) We hypothesize that blocked study will result in significantly higher scores on algebra tests than random study during practice....
14) We hypothesize that blocked study will result in significantly higher scores on word problem tests than random study during the transfer test.
Clearly, listing 14 measurable hypotheses would be repetitive and tedious. However, using a graphical framework such as a table could help to illustrate our predictions
Structure is one key to clarity despite complexity.
A graphical representation could help to organize our thinking and consolidate our presentation. For example, we could summarize our predictions in a table (Table 2):
Table 2. Study predictions. The symbol ">" denotes "significantly greater than."
The graphical representation represents a concise representation of our experimental predictions.
You might wonder: what are the likely results of the study? Is blocked study better than other study schedules?
Researchers in linguistics, motor control, and education (among other fields) have experimentally addressed similar questions (Battig, 1979; Shea and Morgan, 1979; Rohrer, 2012). Based on many similar experiments, the likely results of of the study would be (Table 3):
Table 3. Study results. The symbol ">" denotes "significantly greater than."
Although blocked practice results in greater test performance during practice, both serial and random practice commonly lead to more retention and transfer! Retention and transfer reflect how well people remember and are able to apply material that they study to different problems. Therefore, retention and transfer indicate how well people learned the material that they studied.
Although serial and random schedules may not seem as effective during study sessions (because test performance during study sessions is significantly lower than for blocked practice), serial and random schedules actually result in greater learning. Practice like serial and random practice can be called "Desirable Difficulties" (Bjork, 1994). Non-repetitive, alternating practice of multiple skills makes practice more difficult than blocked presentation and decreases performance during practice. However, the difficulties are desirable because non-repetitive practice results in more learning than blocked practice.
The graphical framework concisely represents a large set of measurable predictions that support the general hypothesis that "Desirable Difficulties" actually increase learning.
Using graphical frameworks can help to simplify and clarify complex arguments. Consider using graphical frameworks to help structure every section of a scientific paper.
Deductive reasoning can lead to incorrect conclusions through logical "fallacies."
DEFINITION: Logical "Fallacies" are weaknesses in either structure or content that render an argument either invalid or unsound (Giere, 2006).
Unfortunately, deductive reasoning does not always lead to true conclusions. Understanding the logical fallacies that cause some arguments to be invalid or unsound is important for science and critical thinking in general.
Understanding logical fallacies is important for science because scientists seek to avoid errors in reasoning. Scientific studies with faulty reasoning will most likely be rejected during peer-review and not published. Scientific papers with faulty reasoning that are published will become more and more inconsistent with other studies, and will eventually be discovered and dismissed. Therefore, logical fallacies do not benefit science or scientists. Scientists work to avoid fallacies, and structure arguments with the strongest reasoning possible.
Understanding logical fallacies is also important because many people try to use fallacious arguments to support arguments that are otherwise untenable. Unethical politicians or salespeople may use a number of strategies to convince people that flawed arguments are actually sound.
Two general categories of deductive fallacies are: 1) problems with the STRUCTURE of arguments that affect validity; and 2) problems with the FACTUAL basis of the premises that affect soundness.
Structural fallacies result in invalid and therefore unsound deductive arguments.
Structural fallacies are arguments that are not valid even if the premises are true. There are many structural fallacies that can result in invalid deductive arguments (Cederblom and Paulsen, 2011). However, three fallacies that are particularly common or important are:
1) Irrelevant Premise(s) ("non sequitur"): Premises not related to conclusions, leading to an invalid argument. For example:
PREMISE: Prominent climate scientists tried to suppress evidence contradicting the theory of anthropogenic climate change.
CONCLUSION: Climate change is not occurring.
EXPLANATION: In 2009, a controversy (later termed "Climategate") ensued after hackers stole emails from the Climatic Research Unit at the University of East Anglia (for more explanation, see Skeptical Science). The emails were selectively released to create the appearance that climate researchers were manipulating data and suppressing evidence. However, a full review of the emails showed that there was no fraud or scientific misconduct. Therefore, the premise of the argument was untrue.
However, even if the premise HAD BEEN true, the argument would still NOT be sound. Why?
The reason why the truth of the premise that scientists falsified data has no bearing on the soundness of the conclusion that climate change is occurring is because the argument is a non sequitur. Although the premises may seem to be related, the behavior of climate scientists has no influence whatsoever on whether anthropogenic climate change is occurring or not. The climate is changing whether or not scientists act ethically. Therefore, arguments about climate change based on the behavior of individual scientists are not valid.
APPLICATION: Many logical fallacies are, in a general sense, non sequiturs if the fallacies involve premises that do not relate to conclusions (Cederblom and Paulsen, 2011). Therefore, it is important to make sure that premises strongly relate to conclusions.
2) Circular Reasoning ("begging the question"): The conclusion is defined in the premises. For example,
PREMISE: X implies Y: If successful people all achieved high S.A.T. scores, then the S.A.T. test predicts success .
PREMISE: Assume X Success is going to a top university that only selects students with high S.A.T. scores.
CONCLUSION: Conclude Y The S.A.T. test predicts success.
EXPLANATION: Circular reasoning can take many forms. However, circular arguments often involve the conclusion being a re-statement of one of the premises, or one of the premises defining the conclusion to be true. In the previous argument, success is defined as having high S.A.T. scores. Therefore, the conclusion that S.A.T. scores predict success is a meaningless re-statement of the premise. Circular arguments are not valid.
APPLICATION: We often know the conclusion before we begin to formally present an argument. However, we must be careful to prevent the conclusion from influencing our premises.
3) Affirming the Consequent. Affirming the Consequent involves arguments of the form:
PREMISE: If P then C If people smoke, they get lung cancer.
PREMISE: We observe C My grandfather died of lung cancer.
CONCLUSION: We conclude P My grandfather smoked.
(Layman, 2005). What is "Affirming the Consequent?" Why is the argument about smoking and cancer a fallacy?
EXPLANATION: In science, affirming the Consequent is often a fallacy because many causes can result in the same effects. In the example of smoking, there are many risk factors for lung cancer (e.g. asbestos, pollution, genetics, etc.) in addition to smoking (Akhtar and Bansal, 2017). Therefore, getting lung cancer does NOT prove that someone smoked. Arguments that "Affirm the Consequent" are not valid.
Affirming the Consequent is arguably the most important deductive fallacy for science.
Affirming the Consequent seems very similar to the most important valid argument for science: modus tollens. However, Affirming the Consequent and modus tollens differ in one key respect: whether the argument seeks to "prove" or reject the prediction made in the first premise. Rejecting a prediction can be valid, whereas "proving" a prediction is a deductive fallacy.
Affirming the Consequent is therefore important for at least three important reasons:
A) Impossibility of "proving" hypotheses. Affirming the Consequent is one reason that we cannot "prove" hypotheses. Hypotheses are explanations of natural phenomena that lead to testable predictions. We test hypotheses empirically by comparing the predictions of the hypotheses to data that we experimentally measure. We could construct the following syllogism:
PREMISE: Hypothesis H1 leads logically to prediction P1.
PREMISE: Experimental data match prediction P1.
CONCLUSION: We have proven hypothesis H1.
However, the syllogism is a deductive fallacy because it Affirms the Consequent! Even if the data match the predictions of a hypothesis exactly, there could still be some other phenomenon that actually explains the data. It could simply be a coincidence that hypothesis H1 makes a prediction that matches the data. Therefore, we can never experimentally "prove" hypotheses.
B) Affirming the consequent also prevents us from using inductive reasoning to "prove" hypotheses. Please refer to the inductive reasoning section for additional explanation.
C) Correlation does not imply causation. People are inclined to engage in "narrative fallacy," consistently seeking to connect even unrelated events with meaning or causality (Taleb, 2010). However, even when two events are actually correlated with each other, the correlation does NOT mean that one event causes the other to happen. There are an infinite number of spurious correlations that happen to be true by chance. For example, any variable that steadily increases with time will be correlated to the expansion of the universe. However, to our knowledge, the expansion of the universe does not cause most phenomena that steadily increase with time.
"Correlation does not imply correlation" is an example of Affirming the Consequent! We could consider the syllogism:
PREMISE: If phenomenon C1 causes event E1, then C1 and E1 will be correlated.
PREMISE: C1 and E1 are correlated.
CONCLUSION: Therefore, C1 causes E1.
The syllogism has the form of Affirming the Consequent. Therefore, correlation does not imply causation.
APPLICATION: Many logical fallacies can make deductive arguments invalid. Importantly, the logical fallacy of Affirming the Consequent prevents experimental research from "proving" hypotheses or inferring causation simply from correlation. Therefore, to test hypotheses using deductive reasoning, scientists must reject hypotheses using modus tollens.
Factual fallacies result in unsound deductive arguments (even if the arguments are valid).
Factual fallacies broadly refer to deductive arguments whose premises are not true. It may seem unnecessary to term an argument where one of the premises is not true a "fallacy." After all, an important part of scientific progress is making arguments with premises (often assumptions) that scientists later discover to not be true. However, there are some common errors involving untrue premises that are often termed deductive "fallacies." Among the factual "fallacies" are:
1) Over-generalization: a premise is too broad. For example,
PREMISE: All mammals give birth to live young.
PREMISE: Echidnas do not give birth to live young, but lay eggs.
CONCLUSION: Therefore, echidnas are not mammals.
EXPLANATION: The goal of science is often to discover generalizations that we can make. However, true generalizations are extremely difficult to find (easy generalizations are often trivial). Therefore, it is important to be very careful when making generalizations, because the generalizations may not be true. Although it would be convenient to make the generalization about mammals that they all give birth to live young, the generalization is simply not true.
APPLICATION: People are naturally inclined to generalize and stereotype (about the world, other people, and even themselves; Steele and Aronson, 1995). Clearly, over-generalizations can have real and damaging consequences, such as biases against groups identifiable by race, gender, nationality, etc. Because non-trivial scientific generalizations are so rare, arguments involving premises that involve the words "All," or "Never" or "None," etc. are likely to be over-generalizations and therefore fallacies.
2) False Dichotomy: Ignoring the middle ground. For example,
PREMISE: Either we can stimulate the economy or we can combat climate change.
PREMISE: We need to stimulate the economy.
CONCLUSION: Therefore, we cannot combat climate change.
EXPLANATION: False dichotomies involve mis-representing a set of options by asserting that there are only two choices, or presenting a "false choice." In the case of the economy vs. addressing climate change, we do not have to choose between a strong economy and reducing human-caused climate change (BCG, 2018). There are many alternatives that potentially involve stimulating the economy while also combatting anthropogenic climate change (e.g. creating new industries, improving efficiency, etc.).
APPLICATION: False dichotomies are commonly used to try to constrain arguments to two options, where only one option is reasonable. For example, a politician may argue "you either support the war or you do not support our country," knowing that most people would like to support the country.
However, dichotomies can also be extremely useful for science! Dichotomies can help scientists structure research and generate cleanly-testable hypotheses. Therefore, it is critical for scientists to make sure that the dichotomies that they posit are not false: that the dichotomies involve two reasonable alternatives, and do not ignore other reasonable possibilities.
3) Weak Analogy: Falsely considering analogous systems to be the same. For example,
PREMISE: Complex things like wristwatches need designers.
PREMISE: Organisms are much more complex than watches.
CONCLUSION: Organisms have a designer.
EXPLANATION: Analogies can help us gain an intuitive understanding of unfamiliar concepts. However, for an analogy to be a valid representation of the system that it helps to explain, the analogy must embody the fundamental parts or processes of the system. Although it is true that wristwatches and organisms are both complex, wristwatches are built in a fundamentally different way than organisms are, and do NOT embody the developmental processes of organisms. Wristwatches are constructed from pre-fabricated parts that must be designed beforehand to fit together. Organisms, on the other hand, develop from continuous interactions among inherited genes, proteins, and the environment. The phenotype of an organism is not pre-determined like a wristwatch is. Although a wristwatch may seem hueristically "representative" of an organism, it is not. Therefore, a wristwatch is a false analogy for an organism.
APPLICATION: Weak analogies are commonly used to sell products or services that are not evidence-based or based on empirical principles (Kelso, 2018). Weak analogies appeal to false similarity, and take advantage of situations where audiences may not be well-informed about the nature of objects or processes (such as organismal development).
A common argumentative strategy is to present valid, but unsound arguments. Audiences can be convinced to accept unsound conclusions based on valid arguments that have untrue premises.
Inductive reasoning can lead to new knowledge.
DEFINITION: Inductive reasoning constructs arguments that are "knowledge expanding" (Giere, 2006). Knowledge expanding means that conclusions of arguments exceed the sphere of the premises. In experimental science, inductive reasoning typically involves generalizing based on a set of observations (although other forms of induction such as analogies are possible; Moore and Parker, 2017).
For example, we could construct an inductive argument about why we are confident that the sun comes up in the morning:
PREMISE: Every morning for recorded history, the sun has come up.
CONCLUSION: Therefore, the sun always comes up in the morning.
Our set of observations is all days in recorded history where the sun has come up (hundreds of thousands). From our repeated observations, we conclude the more general statement that the sun always comes up in the morning.
QUESTION: Is it reasonable to consider the statement "the sun always comes up in the morning" to be true?
Clearly, inductive reasoning is very useful. Once we have confidence in a statement like "the sun always comes up in the morning," then we can use the statement to make predictions and plans. For example, we can count on having enough light tomorrow morning to safely bike to work.
We can use inductive reasoning to come to LOTS of conclusions to try to expand our knowledge. For example, three more arguments that also use inductive reasoning could be:
1) PREMISE: All pigeons in central park are black.
CONCLUSION: Therefore, all pigeons are black.
2) PREMISE: All of the students who come to office hours really like my class.
CONCLUSION: All of the students in my class really like the class.
3) PREMISE: I always run red lights.
CONCLUSION: Everyone always runs red lights.
Are all of the conclusions of these three inductive arguments true?
It might seem that there are some problems with the three inductive arguments listed above. For the first argument, it may not seem reasonable to extrapolate from one population to an entire species (i.e. pigeons). For the second argument, it seems entirely possible that students who don't like the class simply do not come to office hours. The third argument crosses the line to ridiculousness: judging everyone's behavior on one person is clearly not reasonable (even if the person is you). Therefore, not all inductive arguments lead to true conclusions. Inductive arguments may lead to false conclusions even if all of the premises are true!
Because the conclusions of inductive arguments can be false even if all of the premises are true, we might be tempted to dismiss induction altogether as fallacious reasoning. However, there is one slight problem:
Induction is the only way we understand anything about the universe (Hume, 1748)!
As pointed out by the philosopher David Hume, we ultimately acquire ALL of our general understanding from inductive reasoning (although all knowledge we acquire is also influenced by the way we acquire and process information leading to intrinsic biases; Kant, 1787). Statements such as "the sun always comes up in the morning" are developed inductively from observations. Even the most rigorous deductive frameworks need induction to generate hypotheses and rules. Therefore, it is important to understand the possibilities and limitations of inductive reasoning.
Inductive reasoning allows us to expand our knowledge. Through observation and inductive reasoning, we can develop new generalizations. Additional observations that are consistent with our generalizations can strengthen our confidence in the generalizations.
In science, we often term generalizations "hypotheses." One role of hypotheses is to act as explanations or models of the world we live in (Giere, 2001). Inductive reasoning is one way of gaining confidence that our explanations/models of the world are useful (or "true"). We often seek to find a convergence (or "consilience") of different types of evidence that all support the same explanation or model in different ways. Therefore, induction is one way of gaining confidence that hypotheses are useful models of the world.
Consequently, inductive arguments form a continuum. "Weak" arguments have little evidentiary support (few observations supporting the argument). "Strong" inductive arguments have strong evidentiary support (many diverse observations supporting the argument). For example, the theory of evolution has been repeatedly and strongly supported by deductively testing specific predictions (Ayala, 2007). However, perhaps the strongest support for the theory of evolution is inductive: evolution is the only hypothesis that is consistent with observations from ALL aspects of biology: from paleontology to ecology to physiology to molecular genetics and more. The fact of evolution is inductively indisputable.
Scientific models like the theory of evolution are supported by an overwhelming number of different types of observations. However, even when centuries of data from many fields of science are not available, inductive arguments can still be strong. For example, using a structured investigation (e.g. based on Hill's Criteria) to identify distinct areas of evidence that independently test an inductive hypothesis can contribute to the strength of an inductive conclusion (Hill, 1965).
Therefore, strong inductive arguments (and consequently knowledge about the world) are possible.
Inductive reasoning is limited because we cannot "prove" (know with 100% certainty) anything through inductive reasoning! Inductive reasoning is limited in the same way that deductive arguments cannot "Affirm the Consequent."
For example, induction could be seen as many deductive arguments together:
PREMISE: Hypothesis H1 Leads to predictions A,B,C,D,E,F,G
PREMISE: Observations A',B',C',D',E',F' and G' are consistent with predictions A,B,C,D,E,F,G
CONCLUSION: Observations A',B',C',D',E',F' and G' support Hypothesis H1.
However, because the argument has the same structure as arguments that "Affirm the Consequent," the argument is NOT deductively valid and therefore NOT necessarily truth preserving. We CANNOT be 100% sure of anything we determine through inductive reasoning.
The implications of the limitations of inductive reasoning are enormous (as identified by David Hume). We CANNOT be 100% sure even of statements like "the sun always comes up in the morning" (Hume, 1748). It is possible that that there is some quirky law of physics that will cause the sun to not come up on the morning of October 20, 3040 because it is such a nice number. Moreover, it might be impossible for us to discover the law before 10/20/3040. However, we cannot prove that something does NOT exist either. Because there are an infinite number of things that are possible but we simply don't know, we cannot be 100% sure of even the most certain information that we have.
In terms of science, there is no way to "prove" hypotheses using induction for the same reason that there is no way to "prove" hypotheses using deduction. There is simply no way of being 100% certain of ANYTHING in science. Therefore, although "proof" remains useful and valid in the realms of mathematics and formal logic, in experimental science we can only strengthen our confidence in particular models, not achieve the certainty of "proof" (Giere, 2006).
Consequently, the terms "valid" and "sound" do not apply to inductive arguments. Inductive arguments cannot be either valid or sound, only "strong," "weak," or some intermediate judgment of strength (Layman, 2005). The word "prove" should be reserved for mathematics, and should not be used when discussing empirical conclusions.
Similar to deductive reasoning, inductive reasoning has common flaws that are sometimes called "INDUCTIVE FALLACIES." Knowing about common inductive fallacies may help to identify and avoid them when making inductive arguments.
Inductive fallacies involve making conclusions about a population based on observations that are not representative of the population.
Many scientific conclusions are based on inductive reasoning. Scientists seldom have access to an entire population, and therefore must perform experiments using a sample of the population. Generalizing from samples to an entire population involves inductive reasoning. Therefore, it is important to consider some of the inductive fallacies that can weaken inductive arguments. Three inductive fallacies are:
1) Unrepresentative Samples
Whenever scientists sample a population, there is the possibility that the sample does not represent the overall population in many ways. For example, women have been historically under-represented as subjects in medical studies (Kim et al., 2008). The sample of subjects for medical research is therefore un-representative of the general population (which is over 50% women; U.S. Census, 2010). There may be important sex differences in important areas such as the effectiveness (or dangers) of drugs that scientists and doctors do not know of because of historically-biased samples.
Sex/gender is far from the only ways that samples can fail to represent populations. Among the other reasons are:
A) Individual Differences: sex, age, lineage (race), socio-economic status, body weight and shape, etc.
B) Environmental Differences: physical, ecological, social, economic, etc.
C) Experimental Artifact: Differences in the time or methods of data collection among samples.
Ensuring that samples are representative of an overall population is challenging (and beyond the scope of our discussion). However, two common methods for making samples more representative are:
1) Balancing. If there are important variables that may affect the outcomes of an experiment, then scientists may work to "balance" samples. Balancing involves identifying important variables and ensuring that experimental groups have equal representation of different values of each variable.
2) Randomization. In many cases, scientists may not know the variables that are likely to influence an outcome. Therefore, scientists may select samples for different groups at random from a larger population. With a large enough sample size, randomization seeks to control both for variables that scientists know could influence outcomes, but also for unknown variables that are also influential.
APPLICATION: Because there are so many ways that samples can be un-representative of a population, scientists must work to ensure that experimental samples are as representative of the population as possible.
2) Hasty Generalization (Cherry Picking)
Hasty Generalization involves drawing conclusions from samples that are too small. For example:
PREMISE: Several prominent scientists falsified data.
CONCLUSION: All scientists falsify data.
Clearly, the actions of a few "bad apples" cannot be used to generalize over an entire group. Hasty Generalization is a term for the common practice of coming to unreasonable conclusions based on small samples.
"Cherry Picking" is when misleading generalizations from small samples are made deliberately. For example:
PREMISE: Clarence Thomas and Barack Obama have been extremely successful.
CONCLUSION: We have achieved equal opportunity in the U.S.
Cherry Picking selects small, biased samples to make an argument. There is overwhelming evidence from many areas of society that there are still substantial, systemic differences in opportunity in the United States (Hanks et al., 2018). Cherry Picking a small number of politicians is an inductive fallacy because it does not represent the bulk of the evidence.
APPLICATION: Small samples are often not representative of large populations. Simply by chance, small samples are likely to over-represent at least one variable. Moreover, deliberately "cherry picking" samples to support a conclusion does not constitute a strong inductive argument.
3) Weak Analogy
Analogies are one type of inductive reasoning (Moore and Parker, 2017). However, just as for deductive reasoning, analogies are not necessarily strong ways of making inductive arguments. For example, we could make an argument that insects are good physiological analogs for humans:
PREMISE: Humans have muscles, skeletons, nervous systems, circulatory systems, and lungs.
PREMISE: Insects have muscles, skeletons, nervous systems, and circulatory systems.
THEREFORE: Insects also have lungs.
Although it may seem reasonable to predict that insects have lungs, insects actually don't have lungs. Instead, insects have long tubes called "trachea" that connect the exoskeleton to internal organs and allow for gas exchange (Harrison, 2009). The analogy of insects and humans is weak (at least for gross respiratory physiology) because the areas for resemblance between insects and humans is not specific enough.
APPLICATION: Analogies can be helpful for representing and clarifying concepts or complex things. Although analogies can generate hypotheses, analogies must be quantitative and systematic to be useful scientific arguments.
Inductive reasoning can be challenging. Inductive reasoning is not truth-preserving, and fallacies can affect inductive arguments, . However, some frameworks (such as Hill's Criteria) are available to help structure inductive reasoning with hypotheses.
Hierarchies are are versatile frameworks for structuring information.
DEFINITION: Hierarchies are frameworks that rank items by their importance or inclusiveness (detail).
Many aspects of human society are organized hierarchically. Government, business, academia, and of course militaries are all structured using hierarchical frameworks. Hierarchies are important elements of the success of fields such as engineering and computer science (Abelson and Sussman, 1996). Hierarchies are not limited to human endeavors, but organize social systems throughout the animal kingdom (Hölldobler and Wilson, 1990). Therefore, hierarchies are an extremely powerful organizational framework.
Not surprisingly, hierarchies can also be extremely useful for structuring reasoning and writing for several reasons:
A) Like most frameworks, hierarchies make writing explanatory by putting information into context. Because hierarchies are ubiquitous, the context of a hierarchy is particularly easy for audiences to understand. For example, hierarchies can help to contextualize otherwise-descriptive frameworks (such as lists).
B) Hierarchies help to limit the amount of information that audiences need to consider at any one time. Using a hierarchical framework allows both authors and audiences to focus attention on a specific subset of information. Hierarchies can contribute to using the power of abstraction to create modular arguments.
C) Hierarchies can help to provide an order for presenting information. Using a hierarchical framework can guide authors when presenting information, and also help audiences to understand the logical flow of an overall argument.
Hierarchical conjunctions can help to identify hierarchical relationships in written arguments.
Many types of hierarchies can contribute to effective communication. Three hierarchies that have practical applications to scientific reasoning and writing are TREE FRAMEWORKS, OUTLINES, and LITERATURE GRIDS.
Trees are effective structures for organizing, retrieving, and presenting information.
DEFINITION: "Tree" structures are ways of organizing information, where each element of information connects to a limited (typically, but not necessarily, 2) number of other elements (Thareja, 2014). The connections follow systematic rules that contribute to organizing the structure.
Tree structures are very powerful. For example, consider the task of finding a telephone number based on a name (e.g. "Kepler") in an alphabetically-organized contact list. The contact list contains names and their corresponding telephone numbers in alphabetical order. For simplicity, imagine that our contact list has only has 26 names (one for each letter).
If we were to look for a name to find the corresponding telephone number, we could start at the beginning of the contact list ("A") and read each name until we got to the name we were looking for. To find "Kepler," we would have to read through 11 letters (A through K) including "K" for "Kepler."
Imagine that we needed to search for the phone numbers of every name in the contact list. If we started each search with "A," half the time the letters would be in the first half of the alphabet (A-M) and half the time the letters would be in the second half of the alphabet (N-Z). The average number of letters that we would have to go through to find the desired name would be (1+2+3+4+...+26)/26, or 13.5, close to half of the total number of letters in the English alphabet (26 letters total).
However, imagine that we organized the contact list in a tree structure (Figure 1). The tree structure has a simple rule: start at "P." For each letter you are at, if the letter you are looking for comes before the letter you are at, follow the left branch. If the letter you are looking for comes after the letter you are at, follow the right branch (you might actually use a similar algorithm to do something like search for a name in a telephone book if you open the book half-way, look at which letter you are at, open either the left half or right half etc.).
If you examine the tree structure, you can see that the most you have to read to get to any letter of the alphabet is 5. By using a tree structure, we have cut our work by more than half!
Using a tree structure may seem too much trouble to avoid reading fewer than 8 elements on average. However, the true power in tree structures is how they scale: how they perform as the tree gets bigger and bigger.
Imagine a contact list with one million names (N). If you start reading from "A" each time, the number of names that you have to read in a contact list before getting to the name you are looking for will average about half of N, or about 500,000. That's a lot of names. However, it turns out that if you build a tree structure with a million names, the number of names you have to read before finding the name you are looking for is approximately log2(N), or about 20! Instead of cutting our work in half, building the tree cut our work by a factor of 25,000! Using a "binary tree" substantially reduced the amount of information that we needed to process to search the contact list (Shannon, 1948).
Clearly, tree structures are powerful. Similar ideas allow you to do things like search the Internet (!).
Although clearly we do not have to deal with as much information as a million-name contact list (or the Internet) in scientific communication, we can still take advantage of the power of tree structures to reduce the amount of information necessary to understand any aspect of a paper or presentation (Dumont, 2009; Wigmore, 1913).
For example, consider that we were conducting research on childhood obesity (a major public health issue; Centers for Disease Control, 2018). Child obesity is a complex issue. However, using a tree hierarchy could help us organize our approach to conducting research and constructing arguments about obesity.
We could first consider the contributors to obesity. Obesity can occur if people take in more energy (calories) than they expend, resulting in the body storing excess calories as fat. There are two general contributors to energy balance: energy input (how much someone eats) and energy output (basal metabolism and how much someone exercises). We could visualize our two factors using a tree:
Creating the dichotomy between energy input and energy output allows us to consider each contributor separately (although there are some connections between the two; Astorino et al, 2018). Consider that we first focus on energy input. Three categories of consumption that affect energy intake might be snacks, drinks, and meals.
Again, creating a tree structure can help us consider each contributor to energy intake separately. Note that we do not have to use a binary (two-branch) tree! In this case, we have three categories of energy input: Snacks, Drinks, and Meals (although limiting ourselves to three or fewer categories is helpful). Snacks, Drinks, and Meals are still fairly large categories, so we could focus even more closely on one category like Drinks.
Within Drinks, we might reasonably hypothesize that soda consumption contributes to obesity. Our general hypothesis could be supported by two arguments: (1) that there is a correlation between soda consumption and obesity; and (2) that there are plausible physiological mechanisms that link soda consumption and obesity. We could express the two alternatives as a part of our evolving tree:
Now that we have two arguments, we can organize our evidence. In this case, we could use inductive reasoning to support each argument based on specific premises supported by published studies:
Once we have our findings organized into a tree structure, we can then use the tree structure to help us express our arguments as paragraphs using logical transitions. Each branch of the tree supports ONE main idea. Therefore, each branch of the tree can become ONE paragraph:
"Both correlational and physiological evidence suggest that soda consumption may contribute to obesity in children.
First, both soda consumption and obesity increased over the past 40 years. From 1970 to 1990 there was a 123% increase in soda consumption among children (Hu and Malik, 2010). Similarly, from 1986 to 2006, the percentage of obese children doubled (Hedley et al., 2004). Therefore, increased obesity occurred concurrently with increased soda consumption.
Second, several plausible physiological mechanisms link soda consumption and obesity. Sugar consumption increases calorie intake (Ludwig et al., 2001). Sugar consumption also changes metabolism to favor fat storage (Brand-Miller et al., 2002). Moreover, consuming sugar as liquid is less satisfying, increasing calorie intake (DiMeglio and Mattes, 2000). Finally, Soda consumption displaces milk consumption among children, reducing the obesity-preventing role of calcium (Borrud et al., 2006). Therefore, the association between soda consumption and obesity is physiologically plausible.
Because there is both an association between soda consumption and obesity, and plausible mechanisms linking soda consumption and obesity, we hypothesize that soda consumption contributes to obesity."
If we wish to visualize the entire tree that we have constructed (so far), it would look like:
If we need to consider other aspects of energy input (e.g. Snacks or Meals), then we could expand those branches of the tree in the same way we expanded the branch for Drinks. Likewise, we could organize the energy output branch of the tree using a similar procedure. In every case, we can compartmentalize our thinking to specific branches of the tree so we do not become overwhelmed with information.
Just as in the contact list example, a tree structure limits the amount of information that we need to process to find (or put into context) a specific finding.
Trees can be powerful frameworks for organizing reasoning and communication. Trees put information into a hierarchical context (in the case of our obesity tree, a hierarchy organized by inclusiveness). Trees clarify the relationships among different types of information, and help to simplify thinking by allowing us to focus in on specific areas of the tree. Therefore, using trees can simplify and clarify the process of reasoning and communication.
Outlines can improve organization and save time.
“One of the most important tips that we can give you regarding development of [a grant] is to begin by creating a bullet outline” (Russell and Morrison, The Grant Application Writer’s Workbook, 2010).
Outlines are one of the most important strategies for improving writing. Even seasoned writers with Ph.D.s can benefit from using strong outlines (Russell and Morrison, 2010). However many developing writers do not begin the writing process by developing a strong outline.
Among the reasons that people do not begin with an outline is the perception that outlines take time, and therefore take time away from the bulk of writing. However, effective outlines can actually save writers considerable amounts of time and stress. Outlines can also be created so that they do not require additional writing (and probably reduce text that must be re-written during revision).
Therefore, outlines can be well worth the investment of time and thought.
How can we create useful outlines? Some suggestions:
1) Make each heading of your outline the clearly-defined CONCLUSION of a REASONED argument.
Authors often begin an outline using very general bullet points. For example, a writer might begin a paper on the importance of maneuverability (agility) for walking and running with the following outline:
INTRODUCTION OUTLINE 1
* Importance of maneuvering
* How animals perform maneuvers
* Previous studies of maneuvering in humans
* Gap in understanding
* General and Measurable hypotheses.
Does the outline seem like a reasonable place to begin writing a paper?
The outline is a reasonable start. HOWEVER, the outline is only a start! What is a limitation of the outline that might suggest ways to improve it?
One of the main limitations of the outline is that the bullet points are too VAGUE. Each bullet point identifies a general topic for a section, but does not provide much guidance for what the specific content of the of the section will be.
The value of an outline is proportional to the specificity of the outline. Vague outlines have much less value than specific outlines.
How can we make our outline more specific and helpful? One possibility that might seem reasonable is to make the outline more procedural: to explain what the plan is for each section.
INTRODUCTION OUTLINE 2
* Explain the importance of maneuvering for animals
* Summarize the research on how animals perform maneuvers
* Summarize previous studies of maneuvering
* Identify the gap in understanding
* Present both the General and Measurable hypotheses.
Is Outline 2 an improvement over Outline 1?
Although Outline 2 might seem more explanatory and helpful than Outline 1, Outline 2 does not contain more useful information than Outline 1. Outline 2 contains the same information as Outline 1, but has more words to read. Therefore, Outline 2 is arguably worse than Outline 1 because it is less concise.
A second approach to revising Outline 1 could be to add more detail in the form of adding more sub-sections in the outline:
INTRODUCTION OUTLINE 3
* Importance of maneuvering
* Avoiding predation for animals
* Sports performance in humans
* Avoiding injury in the elderly.
* How animals perform maneuvers
* Mechanisms of motor control
* Changing the direction of movement
* Rotating the body
* Making changes to forces within a step
* Making changes to leg placement between steps.
* Previous studies of maneuvering in humans
* Houck, J. (2003).
* Besier et al., (2001)
* Rand and Ohtsuki, (2000).
* Gap in understanding
* We don't understand the mechanisms used to control maneuvers.
* General and Measurable hypotheses
Is Outline 3 an improvement over Outline 1?
Outline 3 contains more information than Outlines 1 and 2, which seems an improvement. However, more information also means more complexity, and more to read and organize within each section. We sometimes also have the tendency to not want to change text that we have written. More text may mean more resistance to change (even if the outline isn't ideal).
Therefore, although the information in Outline 3 definitely could be useful, more information could actually make the main points of the outline more difficult to defend or change as necessary.
Moreover, what logical transitions does the outline use?
We might notice that the outline primarily uses AND conjunctions, the weakest form of conjunction. An outline featuring a "laundry list" of AND conjunctions might not be the most powerful way to start.
Make the headings of your outline the clearly-defined CONCLUSIONS of a REASONED argument.
One way to add information to an outline is to add information so that new elements contribute to developing and supporting the conclusions of reasoned arguments. Developing arguments at the outline stage takes a bit more thought than simply adding information. However, the investment of thought and time pays off considerably by making subsequent writing easier.
An outline structured around conclusive sentences of reasoned arguments might look like:
INTRODUCTION OUTLINE 4
* Maneuvering performance is important for both animals and humans. For animals, maneuverability affects fitness. For humans, maneuverability can constrain performance and contribute to injury.
* Maneuvering involves both changing movement direction and appropriate body rotation.
* To perform maneuvers, humans could either change foot placement or change foot forces during stance.
* The role of anticipatory adjustments relative to changes that occur during the stance period of a turning step remains unclear.
* General and Measurable hypotheses.
Is Outline 4 an improvement over Outline 1?
I argue that outline 4 is more helpful than Outlines 1, 2 or 3 because Outline 4 provides a set of conclusions or goals that we can use to organize our information. As we review and include the results of research from our literature grid, we can potentially fit information we find into a reasoning structure from the beginning. Moreover, we have already identified some dichotomies (OR disjunctions) that make our arguments more compelling.
However, Outline 4 could still be stronger. To make our outline as strong as possible, we can move to our second suggestion:
2) Use a HIERARCHICAL framework.
Hierarchies are powerful ways to structure information. Taking advantage of hierarchies in our outlines is as simple as making sure that sub-headings of our outline are hierarchically related to larger categories. The "Rule of Three" can also be helpful for structuring our hierarchy: when possible, limit sub-categories to three or fewer elements (although more than 3 premises for inductive arguments is OK).
As we do research (aided by our literature grids), we can make our outline stronger by thinking about ways to make the outline more hierarchical. Our outline will become stronger and stronger if each element of information that we add contributes to supporting conclusion statements in a clear hierarchy.
Our outline will also become stronger if we continue looking for ways to use our most powerful transitions: "therefore", "but", and "or."
A first step to make our outline more hierarchical, and clearly identify strong transitions, might look something like:
INTRODUCTION OUTLINE 5
* Maneuvering performance is important for both animals and humans.
* [CLARIFICATION (Definition)]: Maneuvers involve behaviourally generated changes to speed, direction and/or body orientation (Qiao et al., 2014).
* For animals, maneuverability affects fitness.
* Demes et al., 1999
* Losos and Irschick, 1996
* [AND] For humans, maneuverability can constrain performance and contribute to injury.
* Besier et al., 2001
* Colby et al., 2000
* Cross et al., 1989
* Maneuvering involves both changing movement direction and appropriate body rotation.
* Maneuvering involves changing movement direction.
* Kane and Scher, 1970
* Greene and McMahon, 1979
* Walter, 2003
* [BUT] Maneuvering also involves appropriate body rotation.
* Jindrich et al., 2006
* Qiao et al., 2014
* To perform maneuvers, humans could either change foot placement or change foot forces during stance.
* To perform maneuvers, humans could change foot placement
* Orendurff et al., 2006
* Patla et al., 1999
* Houck, 2003
* Rand and Ohtsuki, 2000.
* [OR] To perform maneuvers, humans could change foot forces during stance.
* Jindrich et al., 2006,2007
* HOWEVER, the role of anticipatory adjustments relative to changes that occur during the stance period of a turning step remains unclear.
* General and Measurable hypotheses.
The outline is still only a start. We can continue to add references to the outline, and modify our conclusions as necessary. However, once we have surveyed the literature and finalized our outline, our outline will provide specific guidance for how to organize our premises and conclusions into reasoned arguments.
Moreover, because we have conclusive sentences as bullet points, we have sentences that we can USE in our written document! The conclusive sentences can remain in our paper as subheadings or topic sentences. We finish the outline with a strong start on the paper itself. Moreover, none of the text in outline need be wasted: we can use our outline directly in our paper.
Outlines need not be limited to indented text! For example, "paragraph frames" can help to organize arguments by separating the overall argument structure from the specific evidence that supports a conclusion (Smith and Imbrenda, 2018). An example of a graphical framework that could help to create a strong argument would be a table:
Paragraph Framework for a Reasoned Argument
Premise/ clarification Type (A,F)* or (D,E) #
(simple and specific)
Transition (Therefore, And, But, Or, Clarification)
(Optional: Clarification 1)
(Optional: Clarification 2)
(Optional: Clarification 3)
* Key for Premise Type: A = Assumption, F = Fact (i.e. from your data or others’ data).
# Clarifications within paragraphs are commonly definitions (D) or examples (E).
Each section of an outline could consist of tables that graphically represent individual paragraphs.
Writing an outline does not need to involve writing text in addition to the paper itself. If outline bullets are written as specific, conclusive sentences, then the bullets can remain to clarify and help structure the resulting paper.File: 1
Literature Grids can help organize research for creating hierarchical reasoned arguments.
Performing library research, data analysis, and writing is typically an iterative process. Published scientific papers often come to very different conclusions than the questions that initiated the research process in the first place. Therefore, REVISION, RE-REVISION, RE-RE-REVISION (etc.) are essential parts of the scientific process (Alley, 2018).
Sometimes people have the mis-perception that once a study has been designed to test a particular set of hypotheses, that the hypotheses (and therefore the conclusions) cannot subsequently change. For example, in some cases (such as some clinical trials or other types of studies that build on a large body of research), hypotheses cannot be reasonably or ethically changed.
However, for many types of exploratory research, the process of data collection and analysis may reveal limitations in the research questions and hypotheses, and require the questions and hypotheses to be changed. Discovering that we haven't asked a question or framed a hypothesis in the most useful way is still a discovery! Discovering a new hypothesis framework that accounts for the data actually collected is not unethical, and can be an important source of inspiration for research. Therefore, it is important to keep extensive records of all aspects of research, so that changes do not require us to start the research process over.
For example, our understanding and use of literature sources can change over the lifespan of a paper. Consequently, it is helpful to document and organize the process of literature research (in the same way it is necessary to document other experimental procedures). Documenting literature research by taking notes (e.g. summaries, etc.) can be helpful if there is a need to re-visit the research in the future. Instead of re-performing library searches to find studies, we can simply refer to our notes.
Organizing the process of literature research can help us incorporate the information we find into reasoned arguments and hierarchies. The process of annotating (adding notes to) literature sources that we find not only can help us understand the research better, but also proactively begin the process of building strong hierarchical frameworks.
One helpful strategy for organizing, documenting, and annotating our literature research is to use the graphical framework of an "Evidence Grid," or a "Literature Grid" (LitGrid). A LitGrid is a table that can guide our research and help us incorporate research into our documents. For example:
Clearly, literature grids can be changed and tailored to the particular needs of an individual project. The LitGrid example above includes a citation, a summary, and then a number of columns that categorize the reference. Deriving categories that can be applied consistently to many studies (so that many studies fall into the same category, and there are a limited number of categories) allows the LitGrid to be easily SORTED, and similar studies GROUPED. Spreadsheet like Google Sheets can almost instantly sort and re-sort a large number of references based on any number of categories and sub-categories.
Investing in LitGrids or other methods of annotation can be very helpful both for creating and revising hierarchical reasoned arguments.
Simplicity is one key to clarity despite complexity
WHY is simplicity important for scientific communication?
The objective of scientific communication (as one form of technical communication) is to enable an audience to faithfully understand specific information presented by the author. Understanding technical communication can be challenging because people can only process a limited amount of information at any one time (Marois and Ivanoff, 2005). For example, "Miller's Law" hypothesizes that the number of discrete concepts that a person can hold in working memory averages 7 +/- 2 (Miller, 1956). However, the capacity of some types of working memory may be even more limited to as few as 3 elements (Fukuda et al., 2010). Therefore, to faithfully communicate, authors must limit the amount of information presented to audiences at any one time.
Technical communication often involves communicating complex principles or reasoned arguments that can involve a large amount of information. Therefore, to avoid overwhelming audiences with too much information, clear technical communication uses simple frameworks and language (Lebrun, 2011). A useful rule of thumb is:
To present more complex information, use simpler structure and language.
Consequently, scientists and other technical writers consistently work to make their presentations simpler: clearer, more concise, and more accurate. The following sections review WHAT are some general principles that can contribute to simple presentation, and HOW we can simplify scientific writing in practice.
Simple communication involves structural reduction and repetition.
Clearly, there are many ways to simplify communication. Three useful strategies for simplifying communication are:
1) The "Rule of Three": using frameworks with three or fewer elements.
2) Repeating simple frameworks.
3) Expressing one main idea per element (e.g. sentence or paragraph).
The "Rule of Three" provides guidance for simplifying presentations by using three or fewer main ideas.
DEFINITION of the " Rule of Three:" use 3 or fewer important elements at each level of an argument. Repeat important conclusions 3 or more times.
WHY limit and repeat information within a presentation?
The "Rule of Three" is based on the hypothesis that people have a preference for things that are presented in groups of three. Unfortunately, there does not appear to be direct evidence for the "Rule of Three" hypothesis. However, much anecdotal evidence supports the idea that people like tripartite (three-part) structures. For example, the "Five Paragraph Essay" is commonly taught at many educational levels. Five paragraph essays are based on 3 body paragraphs that defend 3 main ideas. Research grant applications are frequently structured around 3 "Specific Aims," or objectives of a project. The U.S. (and other) governments have three main branches, etc. Therefore, important examples of 3-part structures abound.
Of course, there are also many examples of structures with more (or fewer) than three parts (e.g. 4-part IMRaD scientific papers, 5 act plays, etc.). Therefore, despite its name, the "Rule of Three" is not really a "Rule" at all, but more what you'd call "guidelines" than actual rules (Sparrow, 2003). Nevertheless, the Rule of Three remains a useful principle to help reduce and reinforce information presented to audiences.
The "Rule of Three" for scientific communication actually consists of two related guidelines: (1) Use 3 or fewer important elements (e.g. main conclusions of a paper) in each level of an argument; and (2) Repeat elements that are important for audiences to understand and remember 3 or more times.
1) Use 3 or fewer important elements in each level of an argument.
Often, students report that class assignments involve achieving minimum word or page counts (i.e. a "10 page paper). Students sometimes add unnecessary "filler" text to reach required page counts. However, scientific communication presents the opposite requirement. The goal of scientific communication is to defend arguments and report conclusions using as FEW words as possible.
Many scientific journals have strict word limits. For example one of the top scientific journals, Science Magazine, has a word limit for research articles of 8,000 words or fewer. 8,000 words is approximately equivalent to a 32-page paper, which may seem like a lot. However, thoroughly explaining the results of studies that involved years of dedicated work by many scientists in the space of 32 pages can be extremely challenging. Therefore, scientists must typically try to defend their scientific conclusions under severe word number constraints.
When space (for written papers) or time (for spoken presentations) are limited, then clearly the number of conclusions that authors can strongly defend is also limited. Defending ONE main conclusion will allow the strongest argument, because the author can devote the entire paper or presentation to defending the single conclusion. Defending additional arguments is, to a certain extent, a "zero sum game." Introducing more elements of information (e.g. premises, conclusions, or concepts) diminishes the emphasis on (and ability to remember) other elements of information. Therefore, it is important to "pick ones' battles" and focus on presenting ONLY the conclusions that can be reasonably defended given space or time constraints.
However, even if authors seek to defend a SINGLE conclusion (e.g. one general hypothesis), there are usually many elements of evidence that must be organized in supporting arguments. How can authors effectively organize large bodies of supporting evidence?
The Rule of Three suggests that evidence and arguments that support a conclusion will be strongest if grouped in a hierarchy (such as a tree structure), where each level of the hierarchy has three or fewer supporting elements.
For example consider a student who seeks to apply to graduate school in Occupational Therapy (OT). The general conclusion that the student seeks to defend is "I am capable of being an outstanding Pediatric Occupational Therapist." However, clearly there are MANY personal attributes and skills that a student could use to demonstrate that they are capable of being an outstanding O.T. How should the student make their statement more specific? How should the student organize examples of their attributes, skills, and experiences to support their more specific conclusions?
The Rule of Three suggests that the student pick three or fewer specific attributes or skills to defend in their personal statement. With a limited number of attributes/skills to defend, the student can focus on making strong, focused arguments to defend each attribute or skill. Suppose that the student chose "empathetic" as an attribute, and "ability to work with diverse populations" and "demonstrated ability to improve performance" as skills:
After starting with the simple hierarchy of three attributes/skills to defend, the student could use the Rule of Three to organize evidence for each of the three sections. For example, for the first section (in outline form):
1) I treat others with empathy even in challenging situations.
A) I am empathetic to the challenges of others.
Example: Volunteering for 100 hours at Springfield Community Help Organization. Most interactions I had with clients were very positive and rewarding.
HOWEVER, some clients were agitated and verbally confrontational, tempting me to become angry. [SPECIFIC EXAMPLE].
Therefore, I learned that people are often experiencing stresses that we don't understand. I learned that asking questions was often a good way to learn about the challenges that other people face. I learned how to respect and empathize with people even when my initial reaction to their apparent attitude might have been anger.
B) I have learned how to be empathetic even during times when I was challenged.
Example: Working at In N Out Burger for 20 hours per week for the past three years while being a full-time student. I worked in a fast-paced environment with approximately 20 different teams of individuals.
HOWEVER, during stressful times like mid-terms and finals, I found it difficult to be understanding when my co-workers called in sick or asked to change their shifts.
Therefore, I learned to separate work problems from academic stress, and find solutions that were acceptable to both my co-workers and to me.
For each example, the student should make sure that the example supports the overall main conclusion "I am capable of being an outstanding Pediatric Occupational Therapist."
The example of the student's personal statement is fabricated, and would clearly be different for everyone. However, the example illustrates that the Rule of Three can help to organize arguments and to LIMIT the information presented to readers to only information that clearly contribute to defending a single conclusion.
To make the strongest arguments possible, and to prevent audiences from being overwhelmed with information, use three or fewer elements per section of a presentation.
2) Repeat elements that are important for audiences to understand and remember 3 or more times.
Students often express the mis-perception that redundancy (or re-stating information) is forbidden in scientific communication. Given the strict word and space constraints that authors face, it is true that reducing redundancy is desirable in many circumstances. For example, data and statistics presented in tables do not need to be reiterated verbatim in the text of a manuscript.
However, redundancy is sometimes useful and appropriate. Redundancy can help to identify and reinforce the most important conclusions of an argument, and is therefore worth the investment of additional words.
The Rule of Three suggests that the most important conclusions of an argument be repeated at least three times. For example, in the personal statement outlined in section (1), the most important sub-conclusions are empathy, working with diverse populations, and demonstrated ability to improve motor performance. If the student were to use a traditional 5 paragraph structure for their essay, the student could
(A) Identify the 3 most important sub-conclusions in the Introduction
(B) Repeat (and defend) each the important sub-conclusions in the body paragraph that defends the sub-conclusion.
(C) Repeat the 3 main sub-conclusions in the final paragraph (perhaps by explaining specifically how the students attributes/skills might be a good fit for a particular institution).
Repeating each sub-conclusion at least 3 times helps the audience identify that the sub-conclusion is important, and also remember what each sub-conclusion is. Repetition can thus be seen as an important tool in establishing and clarifying hierarchical frameworks.
Although our discussion of each guideline focused on writing, the Rule of Three is even more important for spoken communication (e.g. spoken presentations). Audiences listening to spoken presentations have severe constraints on their ability to understand and retain information. Therefore, presenters must make extraordinary efforts to ensure that audiences have repeated opportunities to understand reasonable amounts of information.
The "Rule of Three" is a useful guideline for reducing and reinforcing information presented to audiences. The Rule of Three suggests that arguments be structured around three or fewer main conclusions. Important conclusions can be emphasized by repeating them at least three times in a paper or spoken presentation.
Repeating consistent frameworks can contribute to simple presentation.
Two of the most important qualities of scientific communication are validity (accurately expressing the ideas that the author seeks to express) and clarity (ability for readers to correctly interpret the meaning of the written or spoken communication). Therefore, communication strategies that support valid, clear communication are helpful for science even if they involve using a communication style that may be discouraged in other fields.
For example, repetition is often thought to be something to avoid. However, repetition is actually a deliberate and powerful strategy for improving the clarity of scientific communication (Silyn-Roberts, 2012). The principle of parallel construction can add emphasis through repetition (Strunk and White, 2000). However, another important benefit of repetition is that repetition can reduce the amount of information that audiences must process to understand communication.
When readers are confronted with a new framework for presenting information, readers must devote time and effort to understanding the structure of the framework in addition to understanding the content of the presentation. Therefore, repeating the SAME framework within a presentation or section of a presentation can reduce the amount of time and effort that audiences must devote to understanding the presentation, and increase the audience's ability to understand content.
Repeated frameworks can be simple. For example, the methods used for experimental studies can be thought of as a set of required steps. However, there are usually many possible approaches available to complete each step of an experiment. Therefore, each experimental step presents a problem: what is the best way to perform the required step?
A simple three-part framework could be useful to explain the methods of an experimental study. First, identify the goal that the methods must achieve. Second, identify one procedure necessary to accomplish the goal. Third, explain the rationale (reason) for choosing the procedure instead of alternatives. Achieving goals may involve several procedures. The simple procedure-rationale framework can be repeated for several procedures. If problems emerge when implementing a procedure, then iterate the framework: identify the sub-problem (1a), solution, and rationale.
Once the methods necessary to achieve a goal have been explained, the framework can be iterated to explain the procedures necessary to achieve a second goal. Graphically, the framework could be represented using a diagram:.
Repeated frameworks can also be useful for other aspects of scientific presentation. For example, the conclusions for measurable hypotheses could use a separate three-part framework (e.g. a syllogism). First, clearly state the hypothesis in full. Second, present and refer to the evidence (e.g. results of statistical tests, illustrated with figures and tables) that correspond to the predictions of the hypothesis. Finally, based on strong reasoning (e.g. modus tollens or equivalent), explain the conclusion that most reasonably follows.
Repetition can strengthen communication at many levels of organization.
One ubiquitous guideline for writing (that is often not followed) is to use topic sentences: sentences at the beginning of a paragraph that provide the theme of the paragraph. For scientific writing, the theme of most paragraphs is the conclusion of a reasoned argument. However, conclusions of arguments typically appear at the end of an argument (i.e. at the end of a paragraph). Is it acceptable to repeat a conclusion as a topic sentence and at the end of a paragraph?
"Book-ending" a paragraph with its conclusion is a form of repetition that can contribute to clear arguments! The topic sentence can provide a "preview" to help readers understand the main argument of the paragraph. The conclusion is then supported both by the evidence within the paragraph AND by the emphasis provided by repetition.
Repetition is only one form of redundancy.
"Redundancy" can be defined as creating several different methods to achieve the same goal. For example, a hospital may primarily rely on electricity from the public electrical grid during normal operation. However, hospitals also have several redundant sources of electricity (e.g. generators or batteries) available in case the public electrical grid loses power. Redundancy can therefore be important when designing important systems or processes.
In the context of communication, redundancy often carries a negative connotation: as unnecessary and/or boring. However, redundancy can be an important strategy for scientific communication (Dumont, 2009). For example, redundancy can involve using several complimentary ways of conveying the same important information. Text, pictures, and even the frameworks used to structure communication can all contribute to emphasizing important information using redundancy. Presenting information in ways that are compatible with each other and use redundancy to support the same conclusions can potentially facilitate understanding and remembering (Packard and Goodman, 2013). Therefore, repetition and other forms of redundancy can help emphasize and communicate important information.
Creating and repeating simple frameworks can help simplify presentations. Repeating frameworks reduces the number of frameworks that audiences must understand, and allows audiences to focus on understanding content.
The principle of modularity (one idea per element) can help to simplify scientific communication.
Modularity involves building arguments from elements (e.g. premises and arguments) that are self-contained. Therefore, making sure that each element contains ONE (and only one) main idea can contribute to clear, modular presentation.
Sentences and Paragraphs are the basic elements of writing (Strunk and White, 2000). Therefore, it is useful to consider some ways to help write self-contained sentences and paragraphs.
1) SENTENCES: ensure that the TYPE of each sentence is well-defined.
In a reasoned framework, most sentences are one of a small number of types. Specifically, most sentences are either
A) A PREMISE, supported by a reference to your data or a peer-reviewed quantitative research study (references placed parenthetically at the END of the sentence).
B) A CONCLUSION, based on clear deduction or induction from supporting premises. For clarity, conclusions can be identified using words like "Therefore" at the start of the sentence.
Therefore, the majority of a scientific paper is constructed from only TWO types of sentences: premises and conclusions. How can we ensure that premises and conclusions are self-contained?
Sentences that are self-contained premises clearly and succinctly state one important piece of information or main idea. For example:
"The spring index was significantly larger than the strut index (p<0.001; Table 1)" expresses one piece of information.
"The spring index was 61±11%, significantly larger than strut, motor, and damper indices (p<0.001 for all comparisons; Table 1)" presents four pieces of information that naturally group together to form one main idea.
Similarly, a main idea can be the conclusion from your own reasoning or from published research. For example:
"The ankle joint is the primary work generator during human walking (Herr and Grabowski, 2012)."
Sentences that are premises can be identified by parenthetical references at the end of the sentence (Brand and Huiskes, 2001). If a sentence in a paper does not have a reference, then make sure it is a conclusion sentence (or question whether the sentence is necessary).
Sentences that are self-contained conclusions directly follow a series of premises (linked together with logical transitions). Conclusion sentences also begin with identifiers such as "Therefore" or "Consequently." For example:
"Therefore, smoking increases the risk of developing cancer."
In addition to premises and conclusions, reasoned arguments can include a limited number of clarifications (e.g. definitions, examples and summaries). Clarifications should also be self-contained and NOT refer to other parts of the paper. In scientific papers, clarifications also commonly require references.
In addition to clearly identifying the purpose of each sentence, it is also important to make sure that sentences are written simply and specifically. A simple rule of thumb that can help ensure that sentences are self-contained is:
Use ZERO or ONE commas per sentence. Occasionally, commas can be helpful to distinguish between transitions and premises. However, additional commas may indicate that the sentence is expressing more than one idea. Therefore, it is clearer to use separate sentences that each express one main idea instead of using two commas (Strunk and White, 2000). Limited exceptions to the principle of at most one comma per sentence include list frameworks, where individual elements of the list may be separated by commas.
2) PARAGRAPHS: ensure that the PURPOSE of each paragraph is well-defined.
Paragraphs are fundamental building blocks of papers (Strunk and White, 2000). Arguments are the fundamental structures of reasoned frameworks (Bilsky, 1963). Therefore, a reasonable approach to writing is to create paragraphs that are self-contained and present ONE reasoned argument. The conclusion of the argument usually follows the premises at the end of the paragraph.
However, to simplify writing (and reading), it is also helpful to use a "topic sentence" for each paragraph. Topic sentences are commonly the first or second sentences of a paragraph, and communicate the purpose of the paragraph to the reader. For scientific writing, BOTH the topic sentence and conclusion of the paragraph should express the SAME main idea.
Why have two sentences expressing the same idea at the beginning and end of a paragraph?
A) Topic sentences help with writing. As an author is writing a paragraph, the topic sentence can help guide the argument. The topic sentence identifies the goal of the paragraph. Therefore, a topic sentence can be helpful for deciding what information contributes to the argument and what information does not.
B) Topic sentences use repetition to provide emphasis. Repetition can help readers step back and understand the main conclusions of an argument despite potentially complex premises.
C) Topic sentences help authors use outlines to structure papers. Powerful outlines use conclusive statements for each section of the outline. Using topic sentences allows authors to directly use much of the text of an outline in the paper itself.
If a paragraph is bounded by conclusive sentences expressing the same main idea at the beginning and end, then clearly each paragraph should defend that single main idea. Therefore, topic sentences can contribute to clear, modular writing.
To simplify writing, make sure that each element has a well-defined purpose. Most sentences are either clearly-defined premises or conclusions that construct reasoned arguments. Each paragraph defends a single conclusion, identified at the beginning and the end of the paragraph.
The principles of simple communication apply to papers, paragraphs and sentences.
In a sense, one goal shared by all the recommendations in the "Reasoned Writing" module is to simplify communication. Simplicity can help both authors and audiences by making writing (and speaking) easier to understand.
A summary of the the overall approach recommended for scientific writing is:
A) Commit to using specific FRAMEWORKS to structure each section (and sub-section) of the paper.
B) For scientific papers, use REASONED frameworks to construct a series of arguments. Arguments can be based on either deductive or inductive reasoning. Support premises with references placed parenthetically at the END of sentences. Make sure that premises are distinct from conclusions. Connect premises using clear logical TRANSITIONS.
C) Use HIERARCHIES, expressed through GRAPHICAL REPRESENTATIONS and OUTLINES to build an overall structure from CONCLUSIVE sentences. Use the Rule of Three to limit the information that readers must understand at any one time. Use repetition to emphasize important conclusions.
In addition to general recommendations, it is also useful to consider some more specific suggestions for constructing simple PAPERS, PARAGRAPHS, and SENTENCES:
Structure is one key to clarity despite complexity.
Papers can contain a lot of information, and therefore can be quite complex. Because papers contain so much information, using a strong structure is particularly important at the level of an overall paper.
Many practices can contribute to strong structures, including creating strong outlines, using hierarchies, and tree structures. Two additional recommendations for structuring papers to contribute to simplicity are (1) Using subheadings; and (2) The principle of "reverse engineering."
1) Use subheadings to create structures that may include two or more paragraphs.
The principle of modularity suggests that each paragraph of a paper be self-contained and defend a single conclusion. However, papers often include arguments that require more than one paragraph to support. How can authors present and emphasize conclusions that apply to several distinct paragraphs?
Using subheadings can help to create structures that may include two or more paragraphs. Subheadings are single sentences that are commonly set apart from paragraphs and further emphasized by using bold or italicized text.
For example, subheadings may help to structure sections of a paper around hypotheses (Qiao et al., 2014; emphasis in brackets mine):
Turning performance was similar among inertia conditions [reject first hypothesis].
Peak braking forces did not decrease as predicted by the turning model [reject second hypothesis].
Force direction relative to the leg did not change with altered inertia [reject third hypothesis].
Just as for outlines and topic sentences, section headings are strongest if they are complete, conclusive sentences. Subheadings are the conclusions of arguments -- simply arguments that span multiple paragraphs. The topic sentences of the paragraphs in a section can be the premises that support the conclusion of the subheading.
Use parallel construction to help readers understand similar topics in different sections of a paper.
Parallel construction involves repetition of similar elements for emphasis and clarity (Strunk and White, 2000). Parallel construction can make both writing and reading easier. For writing, using a parallel construction can help in planning and outlining different sections of a paper. For example, if the Introduction is organized around defending three hypotheses (as in the example subheadings above), then the structure of the Introduction suggests a natural structure for the Results and Discussion: each can have three sections that correspond to the three hypotheses, in the same order as in the Introduction. Therefore, a parallel construction can help to organize and specify writing.
Parallel construction can also help the reader. For example, if a reader has a question about Hypothesis 2 in the Discussion section of a paper, with parallel construction the reader can easily find the data relating to Hypothesis 2 in the Results and justification for Hypothesis 2 in the Introduction. Parallel construction can therefore help both authors and readers communicate ideas.
Using conclusive sub-headings and parallel construction can simplify the process of structuring papers.
2) Reverse Engineering can help ensure that conclusions are adequately supported by evidence.
Perhaps because we typically read papers from the beginning to the end, many people's inclination is to write papers from beginning to end. However, writing papers from the beginning is not necessarily the simplest approach.
A first step to simplifying the writing process is to first break the paper into discrete pieces by creating a strong outline composed of conclusions to reasoned arguments. A strong outline provides a GOAL for each section and paragraph of the paper. However, even creating an outline can seem to be a daunting task. How can we simplify the process of creating an outline?
One approach to creating an outline is to use a "recursive algorithm," or a set of rules that we can repeatedly apply until we have a satisfactory outline. In fact, we have already encountered parts of an algorithm when creating tree structures. We can apply the power of tree structures again when creating outlines.
Imagine that we have performed an experiment to test the following general hypothesis:
"Our general hypothesis is that recalibration and not strategic adjustments contribute to perceptual adaptation during throwing tasks."
At this point, you might have some questions. What is recalibration? What are strategic adjustments? What is perceptual adaptation? Why is perceptual adaptation important? What tasks would be best to use to test the hypothesis? Why would we expect recalibration to contribute to perceptual adaptation? Why wouldn't we expect strategic adjustments to contribute to perceptual adaptation? Why is throwing a good task to use to test the hypothesis?
All of your questions are good questions! The problem that arises is that when you or I (as the author) have reviewed the literature, designed the experiment, collected the data, analyzed the data, tested your hypotheses and come to conclusions -- you already know the answers to all of the questions in the previous paragraph. However, your reader may NOT know the answer to one or potentially all of the questions in the previous paragraph. Therefore, you must ANSWER the questions before expecting a reader to understand your results or conclusions.
Reverse-engineering from the general hypothesis can help us structure an Introduction section of a paper that sufficiently explains the hypothesis. We could start by re-arranging our questions in pre-outline form:
What is perceptual adaptation?
Why is perceptual adaptation important?
What is recalibration?
Why would we expect recalibration to contribute to perceptual adaptation?
What are strategic adjustments?
Why wouldn't we expect strategic adjustments to contribute to perceptual adaptation?
What tasks would be best to use to test the hypothesis?
Why is throwing a good task to use to test the hypothesis?
GENERAL HYPOTHESIS: "Our general hypothesis is that recalibration and not strategic adjustments contribute to perceptual adaptation during throwing tasks."
By working backward (up, in our case) from the general hypothesis, we have created a series of questions to answer. Our outline could be a little stronger by acknowledging that the WHY questions are more important than the WHAT questions. Actually answering the WHY questions would be an even stronger framework for our Introduction. Answering some of the questions could result in an updated outline:
Perceptual adaptation is important for maintaining high performance despite constant changes to internal and external environments.
DEFINITION: Perceptual adaptation is the ability to recover function after a change to sensory input (Stratton 1897).
Strategic adjustments are simple strategies that could potentially contribute to perceptual adaptation.
DEFINITION: Strategic adjustments are short-term behavioral changes that improve performance (Redding 1996; McNay and Willingham 1998).
Perceptual adaptation frequently involves long-term changes consistent with recalibration.
DEFINITION: Recalibration is brain plasticity that changes the sensory-to-motor transformations associated with movement (Bock et al., 2005).
It is unclear whether strategic adjustments or recalibration both used for diverse motor tasks.
Throwing at a target is a complex motor skill that directly depends on sensory input (Urbin, 2012).
GENERAL HYPOTHESIS: "Our general hypothesis is that recalibration and not strategic adjustments contribute to perceptual adaptation during throwing tasks.".
By "reverse-engineering," or working backward from our hypothesis, we now have the beginnings of an outline that we can use to structure our Introduction. We could use the same algorithm within each section of our outline (i.e. "recursively") to create sub-topics populated by more specific conclusions.
"Reverse Engineering" from conclusions can simplify the process of structuring papers and ensure that readers are provided sufficient information to understand the conclusion.
Reverse engineering from conclusive sentences can help simplify paragraphs.
Paragraphs are the basic unit of composition (Strunk and White, 2000). Paragraphs help readers to understand when a narrative transitions from one topic to another. Paragraphs can also help writers organize large amounts of information into manageable units. Therefore, designing clear paragraphs is important for effective writing.
Three recommendations for writing effective paragraphs are:
1) Deliberately select an appropriate FRAMEWORK for each paragraph.
2) Use CONCLUSIVE topic sentences to introduce reasoned arguments.
3) Reverse-engineer reasoned arguments from specific conclusions.
Most of the recommendations for writing paragraphs are also recommendations for other aspects of writing. Therefore, only a brief discussion of each recommendation is necessary.
1) Deliberately select an appropriate FRAMEWORK for each paragraph.
Writing a paragraph can be simplified by deliberately choosing a single framework to structure the paragraph.
To review, chronological frameworks are only appropriate in limited situations where time is critical. Chronologies are not the most powerful or interesting frameworks for most professional communication.
Although lists are useful and appropriate when they support hierarchies, lists should not be the most common framework used in scientific writing.
Most paragraphs in scientific writing are structured using reasoned frameworks. Therefore, deliberately choosing a reasoned framework can simplify writing because chronologies, lists, or other elements do not need to be considered. Sentences that are not critical to the reasoning simply detract from the argument and can be deleted.
The most important part of reasoning is a strong conclusion. Therefore, writing paragraphs can be a systematic process. First, clearly state the conclusion of the paragraph. Second, construct the arguments necessary to support the conclusion (using deductive or inductive reasoning).
2) Use CONCLUSIVE topic sentences to introduce reasoned arguments.
Reading and writing can be confusing because many published papers do not follow some of the most common recommendations made to students of writing. For example, the recommendation to use topic sentences has been made for at least 100 years (Strunk and White, 2000). Moreover, recommendations to use topic sentences are ubiquitous, and part of many popular frameworks for writing (e.g. TEEL, TAXES, AXES, PEEL, TIPTOP, SEED, PIE, etc.).
However, many scientific papers do not have identifiable topic sentences. Why don't scientific papers follow established recommendations?
Writing is a very open-ended process, and scientific writing is continually evolving. Many scientists are not formally trained in scientific writing and could benefit from guidance (Brand and Huiskes 2001; Brand, 2008). Topic sentences may also be omitted from scientific writing simply to reduce word counts.
Even though many scientific papers do not include topic sentences for each paragraph, topic sentences that are conclusive and match the conclusion sentences of the paragraph are still useful. Selecting and specifically wording a topic sentence before writing a paragraph clarifies the GOAL of the paragraph. Starting a paragraph with a clearly-written topic sentence allows authors to determine whether all added text that is instrumental to achieving the goal. Any text that does not support the conclusion can (and should) be removed.
Therefore, structuring paragraphs using topic sentences is a useful FRAMEWORK that can be repeated for most or all paragraphs of a paper. Writing using topic sentences is useful, even if the topic sentences are eventually removed in the interests of reducing words or enhancing flow. Starting paragraphs by writing conclusive topic sentences also helps to ensure that paragraphs remain focused on a single, reasoned argument.
3) Reverse-engineer reasoned arguments from specific conclusions.
Using a conclusive topic sentence also allows authors to use "reverse engineering" to help structure paragraphs. Starting with the conclusion of an argument can help authors identify the definitions and premises necessary to defend the conclusion. Beginning a paragraph with a conclusion essentially forces authors to work backward: starting from the conclusion before carefully organizing the premises that support the conclusion.
For example, consider a paragraph about building a musculoskeletal computer model of the hand (Lee et al., 2015). The authors used an optimization technique, where a computer was programmed to find three-dimensional muscle attachment sites that best corresponded to observed muscle function. However, one discovery from the procedure was that some hand muscles have more than one place that they could attach to bones and still function normally.
The authors therefore sought to defend the conclusion "Optimization demonstrated that multiple equivalent muscle attachments are possible for some muscles." Knowing the conclusion allowed the authors to organize complex data into premises that support the conclusion, resulting in the paragraph:
"The optimization procedure found multiple muscle attachments. For some muscles, attachment points were constrained to a narrow region. For example, the standard deviation for FDS at the MCP joint was 0.6 mm. For other muscles, attachment points could be located in a broad region. For example, the ES at the PIP joint had a standard deviation of 1.7 mm. Still other muscles exhibited distinct alternative attachment regions. For example, RI at the MCP joint showed two alternative attachment regions, resulting in a bimodal distribution and large standard deviation of 9.0 mm. Therefore, optimization demonstrated that multiple equivalent muscle attachments are possible for some muscles."
Therefore, reverse-engineering using conclusive topic sentences can be helpful for organizing complex information into clear arguments.
Using a reasoned framework to structure paragraphs is consistent with other useful recommendations, such as using the three "Cs" for effective paragraphs: Context, Content, and Conclusion (Precise Edit, 2012).
Context: Each paragraph fits within a clearly-defined [reasoned] framework.
Content: All sentences are referenced premises that directly contribute to a deductive or inductive argument.
Conclusion: The conclusion of the paragraph is clear, reasonable, and well-supported.
The task of writing paragraphs can be simplified by preparation. Deliberately organize the paragraph around a specific framework. "Reverse engineer" the arguments of reasoned paragraphs from conclusive topic sentences.
Modern scientific writing seeks to express ideas with sentences and words that are as simple and clear as possible.
In this "Reasoned Writing" module, we largely focus on structuring communication: how to use frameworks and reasoning to make clear written arguments. However, the utility of science overwhelmingly depends on content, not on writing style. Therefore, one of the main objectives of scientific writing is to minimize how conspicuous the writing itself is in communication.
Eloquent and beautiful scientific writing does exist, and could potentially have a larger impact than less pleasurable text. However, the dangers of complex and confusing prose far outweigh the benefits of unusually elegant writing. Confusing writing could lead to mis-interpretation and cause costly wastes of time and effort. Confusing writing could also help to conceal the rare cases of scientific fraud, which can result in direct and indirect public health costs (e.g. sickness and death). Therefore, a reasonable goal for scientific sentences and verbiage is not to use florid prose with a rich and varied vocabulary. Instead, a reasonable goal of scientific writing is to be as simple as possible.
Two ways of simplifying writing at the level of sentences and words is through SENTENCE STRUCTURE and WORD CHOICE:
Some of the most basic sentence structures can result in clear scientific writing.
Many students learn the basics of grammar early in their academic careers, but may not subsequently re-visit the basic components of writing. However, using simple sentence structures can help to clarify scientific writing. Some recommendations for clear writing grounded in the basics of English grammar are:
1) Use a simple sentence structure with a central verb.
2) Use verbs that are clearly members of one of the three basic verb types.
3) Use active voice to make sentences as short and straightforward as possible.
1) Use a simple sentence structure with a central verb.
One goal of scientific writing is to express ideas using the fewest, simplest words possible. Effective sentences are concise and express ONE main idea per sentence. Therefore, using the simplest sentence structure possible will help clarify scientific writing. Several principles can contribute to simplifying sentence structure.
A) A Subject - Verb - Object sentence structure is sufficient for most scientific sentences.
One of the simplest sentence structures in English is:
SUBJECT - VERB - OBJECT (SVO structure).
In the SVO structure, the verb is central to the sentence and connects the subject and the object. For example, in the sentence "Heart disease is the leading cause of death in the United States," "Heart disease" is the subject, "is" is the verb, and "leading cause of death in the United States" is the object.
The SVO structure is a fundamental part of the English language. Scientific writing seldom requires sentence constructions more complex than SVO (e.g. scientific writing does not commonly require intransitive constructions). Therefore, deliberately selecting a SVO structure is a reasonable approach to clear scientific writing.
B) The Subject, Verb, and Object are clearest when as close together as possible.
Having the Subject, Verb, and Object of a sentence as close together as possible improves the clarity of the sentence (Gopen and Swan, 1990). A useful strategy for writing sentences is to start with the KERNEL (central core) of the sentence. Only AFTER creating a clear kernel, add modifiers that put the sentence into context.
For example, a group of students wrote the sentence "Due to this fact, we hypothesized that our unskilled athletes, although dependent on the relationship between muscle activation and interaction torque of the wrist, elbow, and shoulder, will ultimately rely more on the interaction torques in the wrist, elbow, and shoulder and less on the muscle activation of the wrist, elbow, and shoulder to produce a higher velocity baseball throw." How could we improve the sentence?
The Kernel (central point) of the sentence is: "We hypothesized that unskilled athletes will rely on interaction torques more than muscle torques." Therefore, it would be clearest to begin the sentence with the central point. Adding modifiers at the END of the sentence can provide context:
"We hypothesized that unskilled athletes will rely on interaction torques more than muscle torques during high-velocity baseball throws at the wrist, elbow, and shoulder joints."
C) Limit sentences to ONE or fewer commas per sentence.
Commas can be helpful to distinguish between transitions and premises. Commas can also delimit lists, where individual elements of the list may be separated by commas. However, commas can also create sentences that express more than one idea or overly-complicate the sentence by separating the subject, verb, and object. Therefore, use at most ONE comma per sentence unless there is a compelling reason to use more.
D) Repetition can help clarify writing and help audiences focus on content.
Repetition can reduce the effort that audiences must devote to understanding structure, and therefore help audiences focus on content. Therefore, repeating the SVO structure is one way to simplify scientific writing.
If repetition results in text that does not sufficiently flow, flow can be improved in revision. Starting from a simple, repeated framework will help the revised text maintain clarity and accuracy.
APPLICATION: The SVO structure can help ensure that every sentence has a clear and unambiguous subject, a central verb, and a clear and unambiguous object. Subject, Verb, and Object should be as close together as possible in the sentence. One useful method of revision is to identify the subject, verb, and object of each sentence in a paper. If any component is missing or unclear, then the sentence may need revision.
2) Use central verbs that are clearly members of one of the three basic verb types.
One way to group central verbs is into three main types:
1) Being verbs.
2) Action verbs.
3) Linking verbs.
1) Being verbs. The verb "To Be" is arguably the simplest verb in English, and is also the primary being verb. When possible, use the simple verb "To Be," even if using "To Be" results in repetitive sentences. Other being verbs (e.g. "seems," "becomes," etc.) are vague and confusing if used unnecessarily.
2) Action Verbs. In contrast to being verbs, there are many action verbs. Action verbs typically involve a subject doing something to an object. For example, "we measured length with a digital caliper" uses the action verb "to measure" to connect the subject "we" with the object "length" with the indirect object of the specific method of measuring length. Similarly, "smoking causes cancer" uses the action verb "to cause" to connect smoking to cancer. Causal relationships require particular care on the part of the author to clearly and unambiguously establish subjects and objects.
3) Linking Verbs. Linking verbs provide the opportunity for important nuance in scientific writing. Technically, "to be" and other being verbs are also linking verbs. However, the nuance of linking verbs other than "to be" require care to ensure that they are used correctly. Linking verbs connect subjects and objects (or attributes) in a variety of different ways. Some of the more common linking relationships in scientific writing are:
"X is associated with Y." The verb phrase "is associated" links X and Y, but does NOT argue that the relationship is causal, coincidental, etc. Moreover, simply finding an association does not specify the type of association (e.g. linear, logarithmic, exponential, etc.). Association is useful if a clear relationship between two things exists, but the nature of the relationship is unknown.
"X is correlated with Y." Correlations are essential for experimental science. Therefore stating that there is a correlation between two things means something: that you (or someone else) have performed the mathematical steps required to establish a correlation and determine its strength and statistical significance. Therefore, stating that there is a "correlation" is a much stronger statement than simply asserting that there is an "association."
4) A fourth verb type, "helping verbs," can ensure that scientific statements have the appropriate scope. However, as suggested by their name, "helping" verbs work with the central verb to help provide specificity or context. Therefore, helping verbs have not been included in the main verb types. We will review "helping" verbs more when discussing specificity.
APPLICATION: Clear sentences contain central verbs that can be clearly identified as one of the three main verb types. If a sentence is confusing, check to make sure that the verb is clear and central to the sentence.
3) Use active voice to make sentences as short and straightforward as possible.
Passive voice can be useful for writing when the "agent" of writing is unimportant or useful to remain undefined (Knight, 2003). Passive voice can be appropriate for some situations. For example, passive voice may be useful when offering recommendations where the target audience is unknown (such as the current document).
Some scientists and educators argue that using the passive voice helps make scientific writing more "objective" (Leather, 1996). For example, a passive construction for a hypothesis could read "It was hypothesized that...," whereas an active construction would read "We hypothesized that..." However using passive voice to affect objectivity has the potential to create an unnecessary fiction: that science is more objective than it is. Although, being as objective as possible is an important goal, science is a human process and cannot be completely objective. Moreover, the premise that writing with language that seems objective makes science more objective is a non-sequitur. The appearance of objectivity does not affect the actual objectivity of the research.
The most important goal for communicating science is not objectivity, but clarity and fidelity to the procedures and outcomes being explained. Therefore, if an author or authors performed an experiment and analyzed data, the most straightforward and faithful representation of the experimental process is one where the author(s) are the agents of the writing. Therefore, active voice is most appropriate for many aspects of scientific papers.
For example, if you performed extensive research on a topic and developed a testable hypothesis, then the most faithful representation of that process is an active construction: "I hypothesized that..." There is no reason to obscure agency because the agent is known: it is you. Pretending that the hypothesis somehow spontaneously generated is disingenuous.
Likewise, if you made a measurement, the most faithful description of the process is using the active voice ("I measured X...). If you have multiple authors of your paper, then all authors take responsibility for all aspects of the paper. Therefore, if the paper involves a measurement, write "We measured X..." If the authors agree on a conclusion, the appropriate explanation of the conclusion would be active: "We conclude that..." Etc.
Active voice has other virtues that have convinced authorities on writing to recommend active voice for over a century (Strunk and White, 2000). For example, active voice typically uses fewer words than passive voice. For example, a passive construction might read:
"Significant improvements to balance scores were observed after treadmill training” (10 words). The passive construction needlessly obscures the subject.
An active construction might read:
"Balance scores improved significantly after treadmill training” (7 words)." Not only does using active voice make the subject clearer, but reduces the word count by 30%. 30% is a substantial reduction in words.
Not all sentences that use active voice are equally strong. For example, which sentence is best?
1. "It was observed that Group A had a significantly higher average score than Group B” (passive voice; 13 words).
2. "We observed that Group A had a significantly higher average score than Group B” (active voice; 12 words).
3. "Group A had a significantly higher average score than Group B” (active voice; 9 words).
Sentence (3) is clearest. Beginning with a specific subject helps keep the focus on the CONCLUSIONS of each sentence (instead of less relevant and distracting subjects such as you as the authors, or indeterminate observers). The sentence could be followed by an explanation of why the score difference is important, etc.
Active voice is often more straightforward and specific than passive voice. Active voice typically reduces the amount of text needed to support an idea relative to passive voice. Active voice is more faithful to the process of science (more "truthful") than passive voice. Therefore, clear scientific writing uses active voice unless there is a specific reason to use passive voice.
APPLICATION. Use active voice unless there is a specific reason to use passive voice. If a sentence is confusing and uses passive voice, consider revising the sentence to active voice with a clearly-defined subject.
Use simple words to express potentially complex ideas.
Language is complex. Concepts can often be expressed with many synonymous words. Each word for a concept may have a distinct, nuanced meaning and sound. Poetry and literature use word choice and sentence structure to convey ineffable meanings or simply sound beautiful, e.g. to be pronounced "trippingly on the tongue."
In contrast to poetry and literature, scientific and technical writing cannot communicate meaning through nuance. Scientific writing must communicate through plain meaning and common definitions. Therefore, a reasonable goal for clarifying scientific writing is to use the simplest words possible to express concepts.
Technical words can help identify terms that have specific meanings and prevent confusion.
Scientific writing often involves using technical terms. Technical terms and acronyms may seem complex, and students are often frustrated when reading text with technical words whose meanings the students may not know. However, being able to define and use technical terms and acronyms is important for scientists.
Technical terms allow scientists to use words that have a specific and unambiguous meaning. For example, the word "bug" applies to organisms in the specific order Hemiptera. However, "bug" is also used generally to refer to insects. Scientists have difficulty using words like "bug" that could easily be mis-interpreted to refer to any type of insect, and must therefore use the term "Hemiptera" to refer to the order of insects. Therefore, scientists must often use technical terms for unambiguous communication.
The LACK of technical terms can also create problems. For example, the word "efficiency" has a specific technical meaning: the amount of mechanical work produced relative to the amount of non-mechanical (e.g. chemical, thermal etc.) work input (Full, 1992). However, "efficiency" also has many colloquial meanings, causing many students to use the term "efficiency" incorrectly in scientific contexts. Without a technical term for "efficiency," both authors and readers must not only know that "efficiency" has a technical definition, but also what the technical definition is. If readers mis-interpret a technical term as having another colloquial meaning, then readers could mis-understand scientific communication without being aware of it. Technical words can therefore help identify terms that have specific meanings and prevent confusion.
Replace complex words or constructions with simple words when possible.
Scientists do NOT use technical or difficult terms to appeal to authority or try to impress anyone. In fact, using complex words may have the opposite effect and diminish people's estimation of the author (Oppenheimer, 2005). Whereas technical terms may be un-defined outside of specific scientific contexts, clear scientific writing uses non-technical words that are as simple as possible. Therefore, selecting the simplest word to express a concept is one way to help scientific writing be as clear as possible.
For example, you might have noticed that in the first paragraph I used the term "ineffable." "Ineffable" is a beautiful word that captures the sentiment that I sought to express. However, "ineffable" is not a common word, and its synonym "indescribable" might be a better choice.
Similarly many long or unusual words have simple synonyms that would be better choices for clear communication:
Complex word Use simple
Plethora -> Many Congruent -> Similar Inferred -> Concluded Multiplicity -> Several Efficient -> Effective Optimal -> Good
Similarly, many complex constructions are unnecessary and can be replaced with simple words:
Complex construction -> Use simple
Due to the fact that... -> Because Regardless of the fact that... -> Although In the event that... -> When In reference to... -> About It is important that... -> Should Is able to... -> Can It is possible that... -> May Subsequent to.... -> After In congruence with... -> Consistent with
... and many, many others.
Use a thesaurus only to find simpler word choices.
If you have learned the basics of the English language, you should NOT need to use a thesaurus for scientific writing. Again, although reading and writing in specific scientific fields may require learning a specific technical vocabulary, the strongest English words for scientific writing are the most common and simplest. Therefore, if you know a simple and common word or expression, it should not be necessary to use a thesaurus to find an alternate or more complex expression.
Use a thesaurus only if you think that you may be able to improve a sentence with a simpler, more common word or expression.
Simplicity is one key to clarity despite complexity. Using technical terms may be necessary to clearly express scientific concepts and findings. However, non-technical words should be as simple, common, and clearly-defined as possible. When revising text, try to identify potentially complex words or constructions and replace them with simpler words.
Specificity is critical for scientific communication.
DEFINITION: Specific writing allows an audience to accurately understand what the authors seek to convey without needing additional explanation.
Specificity is critical for scientific communication. For example, the failure to specify the units for measurements caused NASA to lose the Mars Climate Orbiter spacecraft in 1999 (Physics Today, 2016). Throughout science, specific written explanations are an essential part of archiving information that is useful to future researchers, engineers, clinicians and decision-makers. Specific communication is necessary to clearly convey the exacting measurements and careful analysis that are important for scientific progress.
However, specific expression is often one of the weakest areas of communication for developing scientific writers (McMillan, 2016). One reason is that when we lack knowledge about a particular topic, we (not surprisingly) also do not understand how much there is to know about the topic (Kruger and Dunning, 1999). We are ignorant even of our own ignorance. Therefore, people who are new to a topic can make inaccurate, over-generalized, or ambiguous statements -- sometimes without even knowing that the statements are unjustified.
Poorly defended, overly-general, or ambiguous statements do not contribute to strong arguments and diminish a reader's confidence in an author. Therefore, unsupported generalities or ambiguous statements not only don't help construct strong arguments, but can actually weaken arguments that the statements are associated with.
Conversely, being able make and strongly defend specific statements can be an indication that an author has the requisite understanding to make strong arguments. Therefore, specific statements are often a necessary (although perhaps not sufficient) component of effective arguments.
Because specificity is so important, it is useful to discuss some principles for specific writing, and to practically apply the principles to make writing more specific.
Specific writing uses elements that are unambiguous, truthful, and self-contained. Constructing arguments from unambiguous statements will help to ensure that the arguments themselves are unambiguous.
1) Specific writing is unambiguous and truthful.
DEFINITION: In the context of communication, "unambiguous" means that there is only ONE possible way for the target audience to interpret an element (e.g. paper, section, paragraph or sentence).
For example, imagine that a student writes the sentence:
"Serial practice leads to higher test performance than blocked practice."
Does the sentence sound reasonable and unambiguous? What is one problem with the previous sentence, given the evidence cited in the section on Graphical Frameworks?
The problem with the sentence is not that it conflicts with the majority of available evidence. The problem is that the sentence is ambiguous. We can think of a number of questions that the sentence does not address: For what types of tasks (e.g. academic, motor, etc.) does serial practice lead to higher test performance than blocked practice? Does serial practice lead to higher test performance for all kinds of people (e.g. old, young, etc.)? Does the level of experience of a learner (e.g. novice vs. intermediate vs. expert) affect the benefits of serial practice? Does the time when test performance is assessed (e.g. during practice vs. immediately after practice vs. days or weeks later) affect the benefits of serial practice? And many more. Each question introduces a different possible interpretation of the sentence.
In essence, the sentence "Serial practice leads to higher test performance than blocked practice," makes a strong, categorical statement that implies: "serial practice always leads to higher test performance than blocked practice," which is simply not true. The sentence needs to be more specific.
Imagine that to make the sentence more specific, the student writes:
"Serial practice usually leads to higher test performance than blocked practice."
By specifying that serial practice doesn't always lead to higher test performance, does including the word "usually" solve the problem? Clearly not, because all of the questions about the first sentence still apply. Moreover, the word "usually" is a vague term, and does not indicate what "usually" means (i.e. how often, exactly?). Instead of making the sentence more specific, the student has made the sentence even more vague.
The student might then decide to revise the sentence to read:.
"Most studies have found that serial practice leads to higher test performance than blocked practice."
Has the student made the sentence more specific? Yes, specifying that "most studies" have found higher test performance with serial practice is more specific than the first sentence. However, there is a problem with the student's choice of specifics: the student has now written a sentence that they cannot defend.
Stating that "most studies" have come to a similar conclusion implies that the author knows that over 50% of studies support the conclusion. To know that over 50% of studies support a conclusion, the author would need to find ALL of the research on a particular topic, read every study, and find that 50% or more of the studies support the conclusion. Even the most experienced scientists in a field are often not confident that they have read ALL the research reports in a field. Therefore, it is highly unlikely that a student has found and read all of the research necessary to make the statement the student made. We do not know that the statement "Most studies" have found something is truthful. Therefore, simply making statements more specific is not sufficient. Sentences must be both unambiguous and truthful.
To make a statement about serial practice and blocked practice, the student must provide enough context so that a reader can faithfully interpret the sentence. For example:
"Serial practice can result in higher performance on retention tests than blocked practice for students learning mathematical concepts (Rohrer et al., 2014)."
2) Specific writing is self-contained.
DEFINITION: In the context of communication, "Self-contained" means that each element (e.g. paper, section, paragraph, sentence) of a presentation either (A) contains all of the information reasonably needed to understand the element, or (B) is part of a framework that contains all of the information reasonably needed to understand the element in such a way that the interpretation of the element unambiguous.
For example, imagine a student who writes a sentence in a paper about exercise:
"This causes sodium levels in the blood to decrease to dangerous levels."
Can you understand the student's sentence? The sentence is difficult to understand because we don't know what "This" refers to. "This" could refer to the sentence before, or three sentences before, or somewhere else in the document. We CANNOT know what "This" means from the sentence itself: the sentence is not self-contained.
Similarly, students often write sentences in the Discussion that are similar to the sentence:
"The data support our general hypothesis."
Without specifying the data being referred to, or what the general hypothesis is, is is impossible to for the reader to be sure which data support what hypothesis. Without an explanation of why specific data support a clearly-stated hypothesis, the Discussion section is not self-contained and not sufficiently specific.
Specific scientific communication seeks to prevent audiences from needing to make ANY guesses about the meaning of each statement (Pechenik, 2016).
Specific scientific writing also minimizes the need for readers to refer to other parts of a document to understand a written element (e.g. sentence or paragraph). For example, a student may develop three General Hypotheses for a paper in the Introduction, and number them "Hypothesis 1," "Hypothesis 2," and "Hypothesis 3." In the Discussion, the student might write a sentence such as:
"The significant difference in throwing accuracy between the trained and untrained groups supports General Hypothesis 1."
Although the sentence is unambiguous and does not require the reader to guess which hypothesis the student is referring to (which is good), the sentence does require the reader to either remember the hypothesis or refer back to the Introduction. A more specific approach would be to re-state the hypothesis before explaining why the data support the hypothesis. Re-stating the hypothesis not only helps make the Discussion more self-contained, but can also help to emphasize the importance of the hypothesis through repetition.
Similarly, using acronyms to condense frequently-used word phrases can be very useful to reduce the amount of text in scientific writing. However, if a manuscript uses many acronyms, it is helpful to provide a reference table with brief definitions of each acronym.
3) Constructing frameworks (e.g. arguments) from self-contained, unambiguous statements helps to ensure that the frameworks themselves are unambiguous.
Somewhat analogous to the "truth preserving" nature of valid deductive reasoning, arguments can be "specificity preserving." If the premises are specific and the framework is strong, then an overall argument is likely to result in a conclusion that can also be considered specific. For example, consider the argument:
DEFINITION: Interleaved practice involves alternating practice trials of two or more different topics. For example, interleaved practice of topic A (trials A1, A2, and A3) and topic B (trials B1, B2, and B3) would practice trials in the order A1,B1,A2,B2,A3,B3 (Rohrer, 2012).
PREMISE: Interleaved practice can result in higher performance on retention tests than blocked practice for undergraduate students learning to differentiate between paintings by two artists (Kornell and Bjork, 2008).
DEFINITION: Retention tests demonstrate that increases to performance persist over time, indicating that performance increases reflect learning (Shea and Morgan, 1979).
PREMISE: Interleaved practice can also result in more learning than blocked practice for 7th grade students studying two contrasting mathematical concepts (Rohrer et al., 2014).
PREMISE: However, blocked practice results in more learning than interleaved practice for undergraduate students using induction to learn how how to correctly pronounce similar French words (Carpenter and Mueller, 2013).
CONCLUSION: Therefore, our General Hypothesis is that interleaved practice is better than blocked practice for learning that involves contrast, but not better than blocked practice for learning that only involves induction.
Without the supporting premises, the conclusion would be vague. However, the specific premises provide a framework with the information necessary to understand what "interleaved practice" is, some types of tasks that involve contrast, and one type of task that only involves induction.
Specificity is necessary for clear scientific communication. Construct arguments using a strong framework and unambiguous statements. Make sure that each element is self-contained, or within a self-contained framework that makes the meaning of the element unambiguous.
Following some practical guidelines can contribute to specific writing.
The principles of specific writing can be summarized as: make each element (papers, sections, paragraphs, sentences) self-contained and unambiguous. However, creating specific elements remains a challenge for many students. A few guidelines can help write specific papers, paragraphs and sentences.
Specific papers provide sufficient information for audiences to understand the central arguments of the paper.
At the level of a paper, specificity means that audiences have enough information to understand the paper (i.e. the paper is self-contained). However, how can an author predict what information an audience needs to understand the argument?
Among the steps that can help authors design a paper to be accessible to audiences are: (1) define the audience; and (2) define all necessarily terminology.
(1) Define the audience.
Clear scientific communication involves using the simplest vocabulary possible to express ideas. One reason to use a simple vocabulary for science is that scientific audiences are potentially very broad. English is currently the standard language (ironically "lingua franca") for for the international enterprise of science. Therefore, scientific papers will potentially have the largest impact if they are written using vocabulary accessible to both native and non-native English readers.
Even when using the simplest English language possible, authors of scientific papers must make decisions about the concepts and technical terms that require valuable text to define or explain. Therefore, selecting a target audience is an important part of planning and outlining a paper.
Clearly, audiences that differ in age, experience, or other respects require different approaches to communication. Here, I will limit discussion to one common audience: the scientific community. The scientific community includes likely readers of archival publications as well as instructors in college-level science courses. Therefore, the scientific community is a large and relevant audience. Moreover, many other resources are available for improving scientific writing in other contexts (Brownell et al., 2013).
One rule of thumb for selecting an audience in the scientific community is: write for scientists in a different field of science.
Writing for scientists implies that you do NOT need to define concepts and terminology that broadly-trained scientists can be expected to understand. We can reasonably expect that broadly-trained scientists will understand mathematical concepts, Newton's laws of physics, evolution and natural selection, stoichiometry, and other basic principles of the natural sciences. We can also reasonably expect that scientists will be familiar with statistical tests and their interpretation. Therefore, we do not need to define concepts and terminology that are included in basic scientific knowledge.
Writing for scientists in a different field of science implies that authors are responsible for defining all concepts and terminology that are NOT included in basic scientific knowledge.
However, students may object that there is no way for them to know what to expect of broadly-trained scientists, because the students are not (yet) broadly-trained scientists themselves! The objection is completely reasonable.
Therefore, one rule of thumb to help students select an appropriate audience is: write for students who would be reading a paper at the very beginning of a course. Terminology that students can reasonably be expected to enter the course knowing (e.g. from prerequisites) may not need to be defined. However, any terminology that was introduced during the course requires definition.
APPLICATION: Clear scientific papers use simple language accessible to ALL audiences. Specifically identifying a target audience can help identify which technical concepts and terms must be identified to understand the paper.
2) Define all necessary terminology.
Writing for people in a different field of science means that you DO need to define concepts and terminology that broadly-trained scientists outside your field can not reasonably be expected to know. For example, the concepts of "interleaved," "serial," or "blocked" practice are specific to fields that study learning (e.g. linguistics, motor learning, education, etc.). Even if you planned to submit a paper to an academic journal in one of these fields, it remains important to define concepts that scientists in other fields would not be expected to know. Defining terms is useful for at least three reasons:
1) Defining terms helps the author control the meaning of each term, preventing confusion if there are multiple definitions for a term.
2) Defining terms can help the authors consistently use terminology. For academic work, defining terms can help students demonstrate their understanding of each term and correctly use each term in a written assignment.
3) Defining terms helps to make research more accessible to a broad range of scientists..
Two practices can help ensure that important terminology are defined:
A) Use the principle of reverse-engineering to identify terms and concepts that must be defined and defended. Starting with conclusions, identify the definitions and concepts necessary to understand the conclusion. Sentences, paragraphs, or even entire sub-sections may be necessary to define and defend important concepts.
For example, if the general hypothesis for a paper reads "Our general hypothesis is that perceptual adaptation is due to recalibration and not to strategic adjustments during throwing tasks," the hypothesis suggests that the paper must define the terms "perceptual adaptation," "recalibration," and "strategic adjustments."
B) Build definitions into repeated frameworks. Definitions rarely stand on their own, but usually support larger arguments. Therefore, at the level of a paper, reverse-engineering definitions can simply become part of a more comprehensive process to reverse-engineer arguments to ensure that the arguments strongly support conclusions.
For example, a general conclusion (Conclusion 1) could depend on three different lines of evidence. We could choose to use one paragraph to defend each line of evidence..
The example of the causes of perceptual adaptation might involve a structure such as:
APPLICATION: Repeating frameworks that include definitions can help to ensure that important concepts and terminology are defined before use.
Specific paragraphs add discrete elements of information to clear frameworks.
Paragraphs are the basic units of written composition (Strunk and White, 2000). In scientific writing, paragraphs are strongest when they defend a single main idea (e.g. a conclusion) that is distinct from ideas defended in other paragraphs. Three practices can help make paragraphs clear and specific:
1) Each paragraph contributes a single main idea to a larger framework.
2) Each paragraph is structured with a strong framework to defend a single conclusion.
3) Each paragraph contains all, and only, the information necessary to understand the paragraph.
1) Each paragraph contributes a single main idea to a larger framework.
Before adding text to a paragraph, it is essential to know how the paragraph contributes to a larger framework. For example, the text you are reading now is part of a list framework identifying three practices for clear and specific paragraphs. Therefore, the purpose of this paragraph is to defend the conclusion that paragraphs should contribute to larger frameworks.
When paragraphs are part of larger frameworks, two things are clear: (A) what the larger framework is, and (B) why the paragraph contributes to the larger framework.
Defining a larger framework for a paper is an important reason to create a strong outline that captures not only the general topics of different sections of a paper, but expresses strong arguments using complete, conclusive statements. Scientific papers are constructed primarily from reasoned frameworks. Therefore, outlines for scientific papers will identify most paragraphs as conclusions of modular reasoned arguments that are also premises for arguments at higher hierarchical levels. Each paragraph will have a defined and specific role in a larger argument.
Although authors can benefit from strong outlines, readers often do not have the benefit of an outline to help understand a paper! For readers to understand why each paragraph contributes to a larger framework, authors must clearly explain how each paragraph is connected to the larger framework. Therefore, paragraphs can be placed in context by including straightforward transitions for each paragraph.
In a list framework, the transition can be as simple as re-stating the element of the list from the higher-level framework. For example, a personal statement or cover letter could be structured using a list framework complying with the rule of three. The Introduction could list the three attributes and skills as conclusions that the person chooses to defend. Each body paragraph could then re-state the conclusion as the topic sentence of the paragraph:
Therefore, when using a list framework, paragraphs do not necessarily need strong transitions from the preceding or following paragraph. Instead, paragraphs have strong transitions that connect each paragraph to the organizing framework.
In a reasoned framework, each paragraph has a clear place as supporting a premise in a higher-level argument. Acting as supporting premises (premises being the conclusions and topic sentences of the paragraphs), paragraphs can therefore connect to to each other using logical transitions: conjunctions (AND or BUT), hierarchical conjunctions, or disjunctions (OR). Therefore, in a reasoned framework, each paragraph strongly connects to the preceding and following paragraphs using a specific logical transition.
APPLICATION. Paragraphs must CONNECT to a larger framework. For hierarchical lists, paragraphs can connect to the more inclusive element of the hierarchy. Strong connections to preceding and following paragraphs are not necessary for lists (but can be helpful). In strong reasoned frameworks, paragraphs connect to preceding and following paragraphs using logical transitions.
2) Each paragraph is structured with a strong framework to defend a single conclusion.
In scientific papers, paragraphs are primarily structured using a reasoned framework to defend a single main conclusion. The premises for reasoned arguments are typically statements of fact based on data or on the conclusions of previous studies. Therefore, most sentences in a reasoned paragraph are premises, clearly indicated by parenthetical references at the end of each sentence.
The type of reasoning (i.e. deductive or inductive) should be clear for the paragraph and any sub-arguments within the paragraph. Therefore, the framework used to structure each paragraph of a scientific paper should be easily identifiable by both authors and readers.
Paragraphs that either (A) do not clearly defend a single conclusion; or (B) address two or more topics are one of the most common sources of weakness in scientific writing. Paragraphs can be extremely useful. The purpose of paragraphs in scientific writing is to delimit individual arguments. If a writer doesn't use paragraphs to defend a single conclusion, then the writer might as well not use paragraphs at all.
APPLICATION: Make sure that each paragraph clearly defends a single conclusion. Clearly state the conclusion of each paragraph at the beginning or end of the paragraph. If a paragraph addresses one or more topic, split it into separate paragraphs that each defend a single conclusion about a single topic.
3) Each paragraph contains all, and only, the information necessary to understand the paragraph.
The principle of modularity can contribute to writing specific paragraphs. Modular paragraphs contain all of the information necessary to understand the paragraph within the paragraph itself.
However, you might reasonably ask: how are self-contained paragraphs possible? For example, what if a term has been defined somewhere else in the paper, do we need to define the term again in a paragraph? Clearly, repeated definitions seems unnecessary.
Considering the "scope" of different types of information can help create self-contained paragraphs that are not unnecessarily repetitive.
DEFINITION: "Scope" is the range of a content where an element of information applies.
For example, we use different definitions of "success" that have different scope. We could define "success" as completing our homework for the day. However, if we discuss a year or a lifetime, we will clearly need to re-define success (unless our expectations are very low). Therefore the scope of the word "success" clearly depends on context, and the context of a single day is too limited to apply to broader discussions.
Different elements of scientific papers typically have different scope.
Definitions typically have a scope that covers (at least) the entire paper. Technical terms are typically defined at most once in a paper. Therefore, as long as a technical term is defined before it is used, the term will have sufficient scope to make re-definition within a paragraph unnecessary. Similarly, the scope of other clarifications such as long examples or summaries is also the entire paper.
In contrast to definitions, premises have very limited scope. The scope of premises is typically limited to the most immediate argument that contains the premise (e.g. a single paragraph). Unlike a definition, premises are not typically assigned to specific words that can substitute for the premise in subsequent text. For example, the word "aforementioned" is sometimes used to refer to a previous premise. However, constructions with "aforementioned" are most often vague and cause a paragraph to not be self-contained. Therefore, vague words such as "aforementioned," or phrases such as "as stated above," or "as previously mentioned" should be avoided.
Therefore, self-contained and specific paragraphs must contain all of the premises necessary to argue for the conclusion of the paragraph. Premises should be stated or re-stated in each paragraph where they contribute to an argument (even if a premise is re-stated more than once in a paper).
Although specific reasoned paragraphs contain all of the information necessary to understand the paragraph, specific paragraphs also contain only the information necessary to understand the paragraph. For example, premises or clarifications that are not related to a reasoned argument are non sequiturs. Not only do non sequiturs not contribute to arguments, non sequiturs can also be red herrings, distracting and detracting from arguments. Therefore, any text in a paragraph that does not directly contribute to understanding the conclusion of the paragraph should be removed.
APPLICATION: Self-contained and specific reasoned paragraphs must contain all of the information necessary to argue for the conclusion of the paragraph. Whereas definitions and other clarifications have broad scope in a paper, premises have limited scope (to the single paragraph that contains the premise). Remove any text that does not directly contribute to the framework of the paragraph.File: 1
Both sentence structure and word choice are important for writing specific sentences.
Clear scientific writing involves clear frameworks, strong hierarchical reasoning, and simple and specific organization at every level (i.e. paper, section, sub-section, paragraph, sentence). Like science itself, clear scientific writing requires attention to detail (Holstein et al., 2015). One important way to express attention to detail is through writing specific sentences.
There are two important aspects of specific sentences: (A) specific sentence structure; and (B) choosing appropriate, specific words.
Specific sentences are structured to be unambiguous and self-contained.
Sentences that we speak often derive their meaning from context (the physical surroundings, or the surrounding words and sentences). For example, we might hand a book to a friend and ask "can you please put this in the bookshelf?" Based on the context, it is clear that by "this" we are referring to the book we are holding. Moreover, our friend can easily ask us to be more specific if they do not understand our request. Therefore, contextual cues are sufficient for much of our informal communication.
However, context can be much less reliable in written communication. For example, some novels have long sequences of dialogue where individual speakers are not identified. Potentially because of limits to our working memory, after several lines of text it can be easy to lose track of which character is saying which line: to lose track of the context. Therefore, readers have limits to their ability to understand sentences based on context.
For scientific sentences to be specific (unambiguous and self-contained), understanding the meaning of a sentence should not require extrapolation from context. The plain meaning of the sentence should be clear from the words of the sentence itself given definitions established in the frameworks that the sentence is part of. Clear sentences commonly share three attributes:
1) The sentence contains one main idea.
2) The subject and object of the sentence are plainly stated and unambiguous.
3) Each sentence contains all of the information necessary to understand the sentence.
1) The sentence contains one main idea.
In scientific writing, clear sentences express a single idea in as few words as possible. Sentences that each contain a single idea can be connected using logical transitions to support frameworks. For example, a student paper about academic and motor (i.e. sports activities) learning included the sentence:
"While students typically have a separate time for academics and motor learning, the question arises on which approach will meet the needs of long lasting learning and performance and whether or not massed or the spacing effect will further enhance performance."
Is the sentence clear and specific? The sentence is not specific because the sentence expresses more than one idea. A clearer construction would be to break the sentence into two sentences that each express a single idea and are connected using a logical transition:
"Students often separate academic study from motor practice. However, interleaving academic and motor practice within a practice session could result in more learning than using separate practice sessions."
Does the sentence "Childhood obesity is correlated with soda consumption" express more than one idea?
The sentence "Childhood obesity is correlated with soda consumption" might seem to express more than one idea because it contains two elements: obesity and soda consumption. However, the correlation between obesity and soda consumption is the single main idea of the sentence. Therefore, the sentence is sufficiently clear and does not require revision (although a reference would be necessary for the sentence to be a premise).
APPLICATION: Write and revise sentences to express a single idea using as few words as possible. Often, writing can be clarified substantially if compound sentences are broken into two (or more parts) connected with clear logical transitions.
2) The subject and object of the sentence are plainly stated and unambiguous.
In clear scientific writing, sentences clearly and plainly state both the subject and the object of the sentence. For example, the sentence "we measured body weight using a scale" has a clear subject ("we") and a clear object ("body weight").
Students are often tempted to write sentences where either the subject or object are determined by context. For example: "Body weight was measured using a scale. These measurements were made immediately before treadmill training."
The second sentence might seem unambiguous because it was preceded by a sentence stating that the authors used a scale to measure body weight. However, the second sentence is still unnecessarily ambiguous: we cannot be sure that "these measurements" refer to body weight. It would be clearer to simply state the subject and object unambiguously: "We measured body weight immediately before treadmill training."
Moreover, typically experiments involve many types of measurements. For example, a more realistic Methods section might include: "We measured body height while participants stood with their back against a wall. We estimated body fat using a skin fold test. We measured body weight using a digital scale. These measurements were made immediately before treadmill training." In the preceding four sentences, we cannot know whether all the measurements were made before treadmill training or not. Because the subject of the sentence is ambiguous("these"), our entire section becomes ambiguous.
Beginning the sentence with "These" results in an unnecessarily ambiguous subject. Using "These" as an object is equally confusing: "We made these measurements immediately before treadmill training." In both cases, the meaning of the sentence and the section is not clear.
A useful rule of thumb is to NEVER use ambiguous pronouns as either the subject or object of a sentence. Ambiguous pronouns include "This," "That," These," "Those," "They," and "Such." .
Similar to ambiguous pronouns, ambiguous constructions include "As stated above," "As previously discussed," or other variants. Sentences that begin with a reference to a previous section of a document cannot be self-contained and therefore are NOT specific.
The most common and easy-to-spot use of ambiguous pronouns is as the subject of a sentence, where the pronoun appears as the first word of the sentence. Simply making an effort to never start a sentence with the word "this" or its relatives can dramatically improve the specificity of many people's writing.
Although not as problematic as ambiguous pronouns, constructions that obscure the subject also weaken sentences. For example, adding the constructions "It is," "There is," or "There are" at the beginning of a sentence is seldom necessary and only serves to make the subject more difficult to identify.
APPLICATION: Do NOT start sentences with ambiguous pronouns (e.g. "This," "That," These," "Those," "They") or ambiguous constructions. Do not use ambiguous pronouns as objects.
3) Each sentence contains all of the information necessary to understand the sentence.
Using ambiguous pronouns or constructions results in subjects or objects being undefined. However, students often write sentences where the object or other important information are simply absent. Without all of the information necessary to understand them, sentences are not specific.
For example, students often write sentences like "Average walking speed increased to 2.5 m/s (P<0.001)." What is the problem with the previous sentence?
The problem with the sentence is that it is missing a reference for comparison. The sentence does not indicate what value the increase is relative to. The sentence is therefore missing important information, and is not self-contained and specific. The sentence could be revised to be more specific: "Average walking speed increased from 2.2 to 2.5 m/s (P<0.001)."
When using words of comparison, it is important to include BOTH elements being compared in the SAME sentence. Without both elements being compared, sentences are not self-contained and specific. Words of comparison include:
"Higher," "Lower," etc.
Similarly, common procedures and calculations often require more information than students provide. For example, many experiments involve more than one different condition. For example, imagine an experiment on walking speed that involved both age (young vs. elderly), vision (eyes open vs. eyes closed), and training (untrained vs. trained) conditions. Two groups (young and elderly) participants trained for one week on two walking tasks (eyes open and eyes closed). Would our sentence "Average walking speed increased from 2.2 to 2.5 m/s (P<0.001)" be self-contained and specific?
One problem with the sentence is that the sentence does not indicate the conditions that the walking speed averages correspond to. The sentence does not adequately specify the condition that the data are averaged over. As written, it is not possible to know which combination of conditions are being compared in the sentence.
A more specific sentence could read: "Average walking speed increased from 2.2 m/s before training to 2.5 m/s after training for elderly participants in the vision condition (P<0.001)." Is the preceding sentence clear and specific?
There is another problem with the sentence. One way to think about the problem is to ask the question: could we write similar sentences for our other variables, such as "Average walking speed increased from 2.2 m/s (no vision condition) to 2.5 m/s (vision condition) for elderly participants after training (P<0.001)" ? The sentence for vision is confusing because it implies that the no vision condition was measured first, and the vision condition was measured later (which wasn't necessarily the case). More generally, the words "increase" and "decrease" imply a change over a continuous variable, whereas in our example the variables "age," "vision," and even "training" are categorical data (comparing two groups).
The "age" and "vision" conditions clearly involve discrete variables that only have a finite number of values. The experiment is not testing young people as they become elderly or both young and elderly people losing their vision. Therefore, because the variables are discrete, the words "increase" and "decrease" are NOT appropriate. Better words for categorical comparisons include "more" or "less," "higher" or "lower." Therefore, the sentence would be clearer if it read: "Average walking speed was lower in the no vision condition (2.2 m/s) than in the vision condition (2.5 m/s) for trained elderly participants (P<0.001)."
What about training? Although training does depend on the continuous variable of time, the training is assessed (measured) in a discrete way and only has two values: before and after training. Therefore, even for training, our sentence would be clearer if it read "Average walking speed was lower before training (2.2 m/s) than after training (2.5 m/s) for elderly participants in the vision condition (P<0.001)."
Can we ever use the terms "increase" and "decrease?" Yes... but only clearly when there is a relationship (such as a correlation) between two continuous variables. For example, "From the years 1970 to 1990, soda consumption increased by 6% per year (Hu and Malik, 2010)."
Let's return to our walking study. Although our sentences are clearer, they are still not specific enough!
What more could we possibly need?
Extensive discussion of research methods is outside the scope of the Reasoned Writing module. However, it is helpful to keep in mind that that there are many ways to calculate averages. Often, procedures for calculating averages (and other calculations) are defined in the Methods. However, if the Methods does not clearly define all calculations, then we may need to specify the data being averaged across. For example "Walking speed averaged across the 10 training trials for each individual, then averaged across individuals, was lower in the no vision condition (2.2 m/s) than in the vision condition (2.5 m/s) for trained elderly participants (P<0.001)." Clearly, calculations that are used for many comparisons should be defined in the Methods to avoid unnecessary repetition in the Results.
APPLICATION: Specific sentences are modular: they contain all of the information necessary to understand the sentence. Sentences with comparisons require BOTH elements being compared in the SAME sentence. Calculations explained in sentences must be clearly defined in the paper or in the sentence.
Specific writing involves choosing words with clear referents and appropriate scope.
Three principles for word choice can help make writing more specific:
1) Only use words with specific referents.
2) Use helping verbs and adjectives to make sure that sentences have appropriate scope.
3) Avoid vague or confusing words.
1) Only use words with specific referents.
The "referent" of a word is the object or concept that the word refers to. In specific scientific writing, words have clear, straightforward referents. For example: "Santiago Ramón y Cajal introduced the term “neuronal plasticity” to describe nonpathological changes in brain structure" has a clear referent: the neuroscientist Santiago Ramón y Cajal.
In contrast, the sentence "Various neuroscientists have used the term “neuronal plasticity” to describe nonpathological changes in brain structure" does not have a clear referent and is not specific. The neuroscientists who have used the term remains unknown. Because of the vague referent "various," the sentence conveys very little meaning and introduces confusion instead of clarity into a text.
Similarly, students are sometimes tempted to use other vague nouns (e.g. in a misguided attempt to make their writing more formal). For example, the sentence "Several important factors affect learning" may seem authoritative. However, in reality the sentence offers very little information to the reader. Moreover, scientific writing does not require effort to sound authoritative. Authority in scientific writing comes from strong reasoning and specific premises, not from writing style. Therefore, vague statements that are meant to sound authoritative actually cause scientific writing to be LESS respectable.
Clear sentences identify the specific referents. For example, the problem with the sentence "Several important factors affect learning" is not with the word "factor" itself, but because the sentence lacks specific referents that specify the meaning of "factor." "Factor" can be used in a more specific way. For example, the sentence "Among the factors that affect learning are (1) student motivation; (2) prior student knowledge; and (3) learning environment" uses a list framework to identify three contributors to learning. The word "factor" helps to indicate that the three identified contributors are only a subset of larger group of factors that affect learning. Therefore, nouns like "factor" can be helpful if part of specific statements, but are NOT specific enough to act alone as subjects for sentences.
Other common words that are also used as vague subjects are "Aspect," "Area," Situation," "Consideration," "Degree," "Case," etc.
Other commonly-used vague subjects are "Various," "Variety," and "Plethora." All three words are commonly used in a vague way without a clear referent. To make most arguments more specific, words like "various" should be replaced with more specific attributes of a group or specific referents. For example, instead of writing "I have worked with various patients during my internship," it is often stronger to argue "I have worked with a diversity of patients during my internship" and then provide specific evidence for your claim to working with a diverse population.
APPLICATION: To write specific sentences, make sure that every word has a specific, clear referent. Avoid vague nouns that are not immediately specified by connecting the noun to a clear referent.
2) Use helping verbs and adjectives to make sure that sentences have appropriate scope.
The concept of scope was helpful to understand how to ensure that paragraphs can be self-contained (remember that "Scope" is the range of a content where an element of information applies). The concept of scope is also useful for selecting helping verbs to help ensure that sentences are truthful. For example, consider the sentence
"Smoking causes cancer."
The sentence "smoking causes cancer" seems like a straightforward, factual statement. However, for scientific writing, the sentence "smoking causes cancer" is not truthful because the sentence is not specific enough.
The sentence "smoking causes cancer" is vague, because it does not identify when smoking causes cancer. Because the sentence is such a simple declarative, it implies "smoking ALWAYS causes cancer," which is not true. The scope of the sentence is too broad. Therefore, the sentence needs a helping verb to make it truthful: "smoking can cause cancer." The helping verb "can" changes the sentence from an unsupported (unsupportable) generalization into a true statement about smoking.
Helpful helping verbs include: "can," "could," "must," "may," "might."
Similarly helping adjectives can also be useful for making sure that every sentence has a truthful scope. For example, consider the sentence:
"Insects have four wings."
Again, "insects have four wings" seems like a specific, clear sentence. The one problem with the sentence is that it is not true. Although most types of insects have four wings, one important order (flies) does NOT have four wings, but two. Instead of two of their wings, flies have modified sensory organs called "halteres" that help flies be extraordinarily stable and maneuverable (Pringle, 1948). Therefore, the sentence "insects have four wings" is not specific enough to include in a scientific paper.
To make our sentence about insects more specific, we could add an appropriate adjective. The adjective that we select depends on the strength and specificity of the statements that we make. For example, sentences with adjectives that specify their scope to different degrees include:
"Some insects have four wings" (very vague scope)
"Many insects have four wings" (vague scope)
"Most orders of insects have four wings (specific scope)
"Only insects in the order Diptera have two wings" (very specific scope)
Using sentences with very vague scope is safe, but not very specific and powerful. Clearly, stronger adjectives like "most" or "only" are preferable if evidence supports the statement. It is best to choose the most specific, informative sentence possible. However, it is necessary to write a truthful sentence. Therefore, truthfulness is the most important criterion, and statements can only be as strongly worded as direct evidence allows.
APPLICATION: Use helping verbs and adjectives to ensure that sentences are as truthful and specific as possible.
3) Avoid vague or confusing words.
Selecting appropriate words to express ideas that are both truthful and specific is clearly important for scientific writing. Conversely, avoiding words that result in vague, non-specific statements can also help to make sure that writing is specific. Some common types of words and expressions that are vague and should be avoided are:
A) Exaggerated or hyperbolic statements: Scientists very seldom have the luxury of making broad categorical statements like "All people breathe," and most categorical statements that we can make are trivial. Therefore, avoid the words "All," "None," "Everyone," "No one," "Always," "Never," or any of their variants.
B) Desires or beliefs: Science is based on using specific evidence to come to conclusions through reasoning. Although some assumptions are necessary for every scientific study, assumptions should be identified and justified. Beyond the assumptions necessary for a study, the beliefs and desires of the scientists should NOT be relevant to the argument. Therefore, there is seldom good reason to use terms such as "want" or "believe."
C) Subjective judgments: Scientific comparisons require specific (usually quantitative) measurements. For example, statistical tests can establish whether one average value is significantly "less" or "greater" than another average value. However, scientists typically avoid using subjective judgments for comparisons in scientific papers. Therefore, avoid the terms "better," "worse," "good," "poor," "beneficial," "detrimental," and their variants.
APPLICATION: Exaggerated or subjective words are vague and confusing. Avoid words that express desires, subjective judgments, or exaggerations.
A systematic approach can help to simplify the process of writing.
There is no known optimal approach to writing. Every person approaches writing differently. Every writing project has different objectives and requirements. Written documents evolve in different ways over time (Graham and Harris, 2016). Scientific writing presents challenges to students because writing often occurs in the context of learning unfamiliar information and concepts.
However, researchers have identified evidence-based practices that contribute to learning how to write effectively (Graham et al., 2015). For example, using defined strategies for planning, drafting, revising, and editing can contribute to writing (Graham et al., 2015).
Structure is one key to clarity despite complexity.
Having a specific approach to writing can help to break down the writing process into more manageable parts. For example, one potentially useful approach that is consistent with the principles that we have reviewed in the Reasoned Writing module could be a "recursive algorithm" that treats a paper as a "tree." By repeating a limited number of steps, we can systematically create and refine each section of a logical tree. For example, five potential steps are:
1) Do the research. Research involves gathering and organizing information. For a literature review or the Introduction section of a scientific paper, research typically involves finding previous studies that were peer-reviewed and published in reputable journals. Literature grids can help to organize the results of past research. Using clearly-defined hypotheses can help to organize the analysis of experimental findings (from the literature or that you have collected yourself).
2) List the independent and dependent variables. Enumerating all variables relevant to the immediate topic or question can clarify your thinking and help to select strong frameworks and create useful outlines. It can also be helpful to rank or prioritize the independent and dependent variables according to importance. First drafts can then focus on addressing only the variables that are most important to the arguments and conclusions of the specific section of the paper that you are working on.
3) Clearly and deliberately create an appropriate framework. Deliberately create a framework to structure the particular level of the written document that you are creating (e.g. the entire paper, a section of the paper, a specific paragraph). Most often, the framework connects the independent and dependent variables, and embodies the logical steps necessary for each connection. Most aspects of scientific papers will require trees and reasoned frameworks (although lists can also be helpful for explaining hierarchies, such as listing the measurable predictions associated with a general hypothesis). Creating conceptual and graphical frameworks helps people meaningfully understand information, and therefore can also contribute to clear communication. Importantly, selecting appropriate frameworks ensures that every element (fact, concept, etc.) is deliberately and clearly connected to one or more other elements to form a recognizable structure.
4) Use collected research to make the framework specific. For papers and sections of papers, specificity involves creating a strong outline. Strong outlines are constructed from statements that are clear, specific conclusions, linked to each other with logical transitions. When using a reasoned framework (a vast majority of the time), the outline clearly identifies the logic of the overall argument of the paper or the section. Using the research from step (1) above, you can informally come to tentative conclusions that form the basis for a specific outline.
Making paragraphs specific involves using specific findings from the research (step 1 above) as premises of deductive or inductive arguments that support the conclusion of the paragraph. Repeat the conclusion of each paragraph in the topic sentence and concluding sentence of the paragraph.
5) Revision. Sometimes the process of specifying one section of a paper involves changing the conclusion of the section. For example, more closely thinking about the research findings used in the section may suggest a different conclusion than initially expected. Moreover, it may be necessary to do more research, which may require changes to the conclusion. Revising conclusions is a necessary and important part of reasoning and writing. However, changing the conclusion of one paragraph or section may have implications for other paragraphs or sections of the paper. Therefore, creating a specific argument may require you to critically evaluate all of the other aspects of the paper, and make any necessary changes (revisions).
6) Focus on the next (more specific) level of the paper, and repeat steps 1 to 5. For example, once you have selected an overall framework for a section and have constructed a strong outline, you can focus on each element of the outline (i.e. focus on defending each conclusion).
Defending some conclusions may require multiple paragraphs, in which case the conclusion from the outline can become a subheading. Repeating steps 1 to 4 involves creating an outline within the sub-section delimited by the subheading, then focusing on each element of the sub-outline.
Defending some conclusions may require a single paragraph, in which case the conclusion of the outline can become a topic sentence. Supporting the conclusive topic sentence involves using your research (from step 1) as premises, organized into deductive or inductive arguments with logical transitions.
The procedure that the Reasoned Writing framework suggests is hierarchical. Beginning by using steps 1 to 5 to outline the most-inclusive ("top") level of the hierarchy establishes a set of specific goals for each section of the paper. In terms of the analogy of a tree, the top-level outline clarifies the overall shape and branches of the tree. Once a set of overall goals and a structure are established, then it is possible to focus attention on specific areas of the outline ("branches") without needing to worry about higher-level arguments. The process of outlining can continue until it reaches a point where conclusions can be specific premises: statements of fact based on data. Sound or strong conclusions that are based on empirical data and self-contained (specific) are modular and unlikely to be changed by sections or conclusions of the paper. Therefore, using a repeated framework to hierarchically generate and fill in an outline can simplify the process of writing by limiting the amount of information that must be considered at any one time.
A systematic approach to writing papers can help to simplify the writing process. One effective approach is to deliberately select a framework for each section of the paper, and use outlines to organize research findings at increasingly-specific levels.
There are several possible ways to navigate the "Reasoned Writing" (RW) module.
Each section of the RW module is numbered. One potential way to navigate the site is to start at the beginning and sequentially follow the numbered sections. Links at the top and bottom (left and right) of each page will take you to the previous or next section in the module. Alternatively, you can use the navigation buttons at the left to navigate the module.
AS A TREE
RW is organized as a tree structure. The tree structure can also be navigated. At the bottom of each section there are either (A) links to sub-branches of the tree; or (B) a short summary indicated by the Application Button (). It is possible to follow branches until reaching an Application Button, indicating that you have reached the terminal branch (or "leaf," if you will) of the tree. Going back to the branch point will allow you to access other branches of the tree.
AS A LINEAR SEQUENCE
It is also possible to access access site content in a continuous sequence. To show all sections of the module, click on the following link:
Show all sections of the "Reasoned Writing" module.
DIVIDED INTO SUB-SECTIONS
Alternatively, breaking the RW module up into several sections may be advantageous. One possible division of "Reasoned Writing" is into five sections. The first section, "Frameworks," introduces three basic frameworks, and why reasoning is the strongest framework for science. The second section, "Reasoning," reviews the basics of reasoned arguments. The third section, "Logic," discusses how to defend conclusions with evidence. The fourth section, "Simplicity," reviews strategies for making arguments that are simple enough for audiences to understand. Finally, the fifth section, "Specificity," reviews strategies for ensuring that arguments are specific enough to convey useful information to audiences.
- File: 1
- File: 1
- File: 1
- File: 1
- File: 1
Few of the ideas in Reasoned Writing / A Framework for Scientific Papers are mine alone.
I have been fortunate to work with some truly wonderful people over the years. I have tried to learn as much as I can from everyone I have worked with. Of course, I have been influenced by my mentors, including Bob Full, Claire Farley, Rodger Kram, David and Marvalee Wake, Jack Dennerlein, Reggie Edgerton, and others. I had the benefit of a strong cohort of fellow graduate students at Berkeley who have been supportive colleagues and friends during my career. I can't thank everyone enough.
Throughout the RW / AFSP module I have tried to provide references to sources for the recommendations. Many sources I have discovered after the fact (I am still and always in the process of learning) -- so I would appreciate suggestions and guidance if there are sources that I have missed.
Some other ideas have specific, un-referenced sources. For example, I was introduced to the "Rule of Three" in a talk by Ken Horch organized by Ranu Jung and Jimmy Abbas.
I am grateful to Lisa Chen, Morgan Dox, Diego Sustaita and Shai Revzen for constructive feedback on the site.
Working with students has also helped me greatly, and provided ideas. For example, the idea for the "paragraph framework" came from an outline assignment by Dalia Liwanag. Other framework ideas came from my Kinesiology 202 course in the Spring of 2018. Working with students is a constant source of inspiration and good ideas, and I thank all of my former and present students.
Finally, I wouldn't have been able to write anything without my family: my wife, parents, sister and family, in-laws and children. I don't live for myself, I live for all of you.