THE RESULTS
Results are comparisons of data to Measurable Hypotheses that "result" in conclusions.
Why do scientific papers have a Results section?
The purpose of the Results section is commonly explained as describing data without interpretation. The Results section is thought to answer the question: "What were the findings?" (Bolt and Bruins, 2012).
If the purpose of the Results section is simply to describe data, then why is the section not simply called the "Data" section?
The reason scientific papers have a "Results" section is because data alone are NOT results.
Whereas "data" refers to objective, quantitative measurements (or "facts"), "results" are outcomes. But outcomes of what?
Results are the outcomes of arguments that use data to test Measurable Hypotheses.
Therefore, a clearer question that the Results section answers is:
WHY do the data lead to the conclusion to reject or support each Measurable Hypothesis?
A single "result" can be considered the outcome of a comparison: between the specific prediction of a Measurable Hypothesis and the measured data.
Being "measurable" means that Measurable Hypotheses can be directly tested by experimental data. Experimental studies typically use statistical tests or other objective comparisons to test hypotheses.
Supporting or testing Measurable Hypotheses does NOT require interpretation or judgment IF the Measurable Hypotheses are specific and testable, and the Results section employs strong reasoning (e.g. rejecting hypotheses using modus tollens). If we set up our reasoning clearly by creating measurable hypotheses that are testable predictions, then after we have collected the data, we have little choice but to accept the conclusion that the data support. In essence, the DATA are making the judgments, not us. Therefore Results can be both explanatory and not involve interpretation.
How can we organize our presentation of a result? One way that we can present results is to simplify the diagram above into a sequential framework: “HYPOTHESIS-EVIDENCE-CONCLUSION” (“H-E-C”; which we have briefly discussed as a way to simplify writing through repetition).
An H-E-C framework for a single result could therefore look like:
For example, if a Measurable Hypothesis (“Measurable Hypothesis 1”) predicts a significant difference between two treatment conditions (X and Y), our reasoning could be structured using the H-E-C framework as:
MEASURABLE HYPOTHESIS
DATA
CONCLUSION
“Measurable Hypothesis 1 predicted that scores for Test Condition X would be significantly higher than scores for Test Condition Y.”
“Scores for Test Condition X were significantly higher than scores for Test Condition Y (t-test; P<0.05). The scores for Test Condition X were 30% more than scores for Test Condition Y (Figure 1).
“Therefore, the data support Measurable Hypothesis 1.”
A General Hypothesis might lead to not only one, but several measurable predictions that are tested by a study. A clear organization for the Results section of a scientific paper could involve repetition of the H-E-C framework for each prediction:
Although the H-E-C framework can help us with the overall organization of the Results, adding information to the framework clearly requires careful thought and effort. We have already discussed some of the principles of creating strong Measurable Hypotheses and Conclusions. Therefore, let’s explore some ways to present data, compare data to measurable hypotheses, and arrive at reasonable conclusions.
We can organize our investigation of data around three main topics:
2) HOW the data compare with the Measurable Hypotheses.
3) WHY the data either support or reject each measurable hypothesis.
