Results are conclusions about whether to reject or support Measurable Hypotheses.
The overall goal of the data and comparisons of the Results section is to explain WHY the data support specific conclusions about the Measurable Hypotheses. Therefore, the conclusions from the tests of the Measurable Hypotheses provide a natural framework for structuring the Results section. Using the conclusions of hypothesis tests as a framework can help focus the text on the most important part of the Results: the specific conclusions that the data support.
A Results section could be organized using a list framework indicated by subheadings, each of which clearly explain the conclusion of testing each Measurable Hypothesis. For example:
Turning performance did not differ among inertia conditions (first Measurable Hypothesis) [supporting arguments] Peak braking forces did not decrease as predicted by the turning model (second Measurable Hypothesis) [supporting arguments] Force direction relative to the leg did not change with altered inertia (third Measurable Hypothesis) [supporting arguments] (Qiao et al., 2014).
Repetition can help clearly structure the text of the Results.
Using a list based on the Measurable Hypotheses can clarify the Results using repetition if the arguments are consistent for each section. For example, if each section uses modus tollens/Strong Inference either to reject (or support) a Measurable Hypothesis, then the common reasoning structure can help the reader understand each section. Other forms of repetition (such as consistent figures or tables) can also be helpful.
Results sections commonly include factual premises (supported by data in the text and/or references to data in tables and figures) that reasonably lead to conclusions about Measurable Hypotheses. Strong premises and conclusions are simple, specific, and connected using clear logical transitions. All data presented in the Results section should clearly contribute to testing at least one Measurable Hypothesis.
Because the Results section has a specific objective of presenting data so that the data clearly test the Measurable Hypotheses, the Results section does not include references to other studies.
Including one (or a very limited number) of examples can help readers better understand the data (e.g. "typical" trials, although "typical" trials should result in measurements that are close to average, not trials that are exceptional in any way). However, scientific conclusions cannot be supported by examples or anecdotal evidence alone. The primary purpose of examples are to help readers understand the data, not to test hypotheses.
Other clarifications are usually unnecessary in the Results. Terminology is typically defined before the Results. Results sections typically do not require summaries (moreover, Discussion sections commonly begin with a summary of the Results). Therefore, the secondary (supportive) role of clarifications is particularly important in the Results.
All data belong in the Results. However, not all conclusions of a paper are in the Results.
Scientists commonly expect all data collected during a study and used to test hypotheses to be presented in the Results (and not in the Discussion). Therefore, it is advisable to test all Measurable Hypotheses in the Results regardless if tests are conclusive or not. If necessary, data used to test Measurable Hypotheses can then be re-visited in the Discussion to support additional conclusions if necessary.
The Results section uses data to defend conclusions that do not require interpretation or judgment (e.g. conclusions that inevitably result from data through sound deductive reasoning applied to Measurable Hypotheses). However, the Results section does not necessarily contain all of the arguments in a paper. In some cases, even when strong conclusions about Measurable Hypotheses cannot be made in the Results, additional arguments in the Discussion can subsequently lead to strong conclusions.
Using data to test Measurable Hypotheses is appropriate in the Results section if tests do not require interpretation or judgment. Therefore, testing Measurable Hypotheses can provide a useful framework to structure the Results.