how to write a results section without interpreting too much 2026

A results section reports what the data showed and nothing more. Knowing how to write a results section without interpreting too much comes down to one rule: describe the pattern, name the numbers, stop before you explain why it matters. Every sentence that says because, therefore or this suggests is a candidate for the discussion section.

Most sections that drift too far aren’t written badly, though. They’re written confidently. The writer hit a clean finding, felt the story click, and started narrating significance before a single citation had been checked. Separating description from meaning takes about twenty minutes of setup and a fixed sentence pattern for every result.

Below is the sequence I use, the phrases that give away interpretation, and a way to strip them out without losing the flow of the argument.

Table of Contents

1. 1What You Need
2. 2Step-by-Step
3. 31. Start with the analysis purpose and sample
4. 42. Report each finding in a fixed order
5. 53. Describe tables and figures without explaining them away
6. 64. Use neutral academic language
7. 75. Separate findings from interpretation
8. 86. Check the section against your research questions
9. 9Common Mistakes
10. 10Frequently Asked Questions
11. 11Can you put results and discussion together in one section?
12. 12How do I report non-significant results without sounding apologetic?
13. 13How do I write a results section for a qualitative study?
14. 14How do I write the results section of a systematic review?
15. 15When writing the results section, which mistakes mean I am interpreting?
16. 16Should I cite literature inside the results section?
17. 17Conclusion

What You Need

Before drafting a single sentence, gather the analysis in one place so the writing stage has nothing to interpret. Interpretation creeps in when you have to remember what an output meant three weeks after you ran it.

Have these five things open in front of you:

• Your research questions or hypotheses, in the exact wording you used in the introduction. You will restate each one before reporting the analysis that addressed it.
• Descriptive output for every group or time point: n, M, SD, ranges, and proportions where the data are categorical.
• Inferential output with the test statistic, degrees of freedom, p-value, effect size and confidence interval for each test.
• A participant flow count, including exclusions and missing data, plus reliability figures such as Cronbach’s alpha and any manipulation checks.
• A blank reporting table. One row per analysis with columns for the question, the test, the statistic, the effect, and the table number. Filling this in first is what stops software commentary from leaking into your prose.

Take fifteen minutes to strip model-generated text out of the output. In SPSS, Stata or R you will usually get lines explaining what a significant result means. Delete them at the source, not in your head, because if you can still see them you will quote them by accident.

Step-by-Step

1. Start with the analysis purpose and sample

Open the section by telling the reader what was analysed and in what order. Name the sample, the measures, and the sequence of analyses, then state that further detail appears in the method section. That last clause does real work: it lets you skip explanations without leaving the reader stranded.

A typical opening runs to two or three sentences: how many participants were included, which measures were used, and that analyses are reported in the order they appear in the method.

2. Report each finding in a fixed order

Use the same five-part sequence for every result and the section stops sounding like an essay. State the hypothesis or research question, give the descriptive statistics, give the inferential result with its effect size and confidence interval, state the direction of the effect, and close with one neutral sentence about what the result indicates.

Worked example. Hypothesis: participants who received the training would report lower anxiety than those who did not.

Descriptives: the training group (n = 32) reported a mean anxiety score of 14.2 (SD = 4.1) and the control group (n = 30) reported a mean of 18.6 (SD = 5.3). Inferential: an independent-samples t test showed a significant difference between groups, t(60) = 3.42, p = .001, 95% CI [1.70, 7.20], d = 0.87. Closing sentence: this result indicates that the hypothesis was supported.

That closing sentence is the ceiling, not the floor. Nothing after it about why the effect appeared, what it means for practice, or how it compares to earlier studies belongs here.

3. Describe tables and figures without explaining them away

A table or figure holds the detail; the text tells the reader what to look at and what pattern it shows. Write one or two sentences per display: what it contains, the main pattern, and the notable values. Anything a reader could see without the sentence does not need to be repeated in words, and anything they could not see belongs in discussion.

Refer to every display in the text, in order, and use the same numbers you refer to. A table that appears in the results section but is never mentioned reads as an afterthought, and examiners do mark that down.

4. Use neutral academic language

Hedging is not weakness here. A description of a pattern in one sample is not a fact about the world, and the wording should show that. Prefer was associated with, participants in the training group reported, and the effect was in the hypothesised direction.

Keep decimal places consistent, use the same term for a variable throughout, and keep alpha and the significance level constant across the section. Mixed rounding and swapped terminology are the most common reasons a reader starts doubting otherwise clean work.

5. Separate findings from interpretation

Run a simple editing pass on every sentence and ask one question: would this still be true if I had a different explanation in mind? If the answer is no, it is interpretation. Sentences that compare your finding to a theory, explain a mechanism, recommend action or praise the size of an effect all fail that test.

Move them, do not delete them. Most interpretation you cut from the results belongs in the first two paragraphs of the discussion, where it does the job the reader is actually waiting for.

6. Check the section against your research questions

Read the headings of your introduction and tick each question off against the section. Every question needs an answer at a descriptive level, including the ones that came back null. Uneven coverage, where one question gets four paragraphs and another gets one clause, is a coverage problem rather than a writing problem.

Also check the reverse direction: nothing should appear in the results that you never asked about. Unplanned exploratory analyses need a label saying so, in both the section and the method.

Common Mistakes

Almost every over-interpreted results section I have marked contains at least four of these. The pattern is consistent enough that learning the phrases is faster than learning the theory behind them.

Sentence you probably wrote	What it signals	Neutral version
This confirms our hypothesis and shows the intervention works.	Interpretation plus a causal claim the design cannot support	This result indicates the hypothesis was supported.
Interestingly, the control group did not change.	Evaluative framing that pre-loads a meaning	The control group’s scores did not differ significantly from baseline.
The large effect size suggests this is a promising approach.	Evaluation and implication	The effect size was large, d = 0.87.
This difference was not significant, meaning the groups are equivalent.	Treating non-significance as proof of no difference	The difference was not statistically significant, p = .14.
As expected from the literature, the effect was strongest in…	Citations belong in the discussion	The effect was strongest in the third subgroup.
This shows that stress causes lower retention.	Causal language from a correlational or experimental design without support	Stress was associated with lower retention.
The result was barely significant, but the trend is clear.	Minimising language that argues with the statistic	The result reached significance at the .05 level.
This is surprising given the sample size.	Commentary on your own study	Delete, or move to the discussion.

A few more habits cause the same problem. Reporting only the tests that reached significance is the fastest way to lose a supervisor’s trust, and readers notice. Pasting raw output straight into the draft brings twelve lines of unedited software commentary with it. Writing every result in a separate paragraph with no signposting turns the section into a list nobody can follow.

Then there is the supervisor who asks for “a bit more meaning” in the results. The compromise that usually works: keep the neutral sentence in the results and put the interpretation in the discussion, where you can point to it explicitly. If your department uses a combined results and discussion section, the same rule applies within each paragraph. Ask which section headings your marking scheme expects and follow those exactly, because that is the version your reader is grading against.

Frequently Asked Questions

Conclusion

Pick one result and write it using the fixed pattern: question, descriptives, test, effect size with confidence interval, one neutral closing sentence. Then read it back and delete anything that explains, compares or evaluates. Do that for a single result first, because once the pattern is in your hands the rest of the section writes itself.