How to write a results chapter — reporting findings without interpreting (2026)

The hardest part of learning how to write a results chapter is learning what to leave out. You have spent months collecting data, you finally have numbers or transcripts in front of you, and every instinct tells you to explain what they mean. That explanation belongs in the discussion. The results chapter has a narrower job: present what you found, accurately and in an order the reader can follow, and stop there. Students lose marks here for two opposite reasons — they interpret everything and leave the discussion with nothing to say, or they paste raw output and leave the examiner to do the work. This guide covers the results versus discussion boundary, three ways to structure the chapter, how to report quantitative and qualitative findings, table and figure conventions, and an annotated example paragraph.

What a results chapter does — and where it stops

Your results chapter answers one question: what did the data show? Not why, not what it means for the field, not what should happen next. Those are discussion questions. The distinction feels artificial until you see how examiners read: they check the results against your methodology chapter to confirm you actually did what you promised, then read the discussion to see whether your interpretation is defensible given those findings. Blur the two and neither check works.

A useful test before you write any sentence: could a reader disagree with this statement while looking at the same data? If yes, it is interpretation and belongs later.

Belongs in results	Belongs in discussion
Descriptive statistics, test outputs, significance values	Why the effect appeared, what mechanism explains it
Themes identified, with supporting quotes	What the themes reveal about the wider literature
Response rates, exclusions, missing data	Whether attrition threatens your conclusions
A statement that a hypothesis was or was not supported	What it means for theory that it was not supported
Tables and figures presenting the findings	Comparison with findings from other studies

Note: “Reporting, not interpreting” does not mean writing without words. A wall of tables with no prose is a failure of the results chapter, not a strict reading of the rule. You are guiding the reader through the findings — pointing out what a table shows is reporting; explaining why it shows that is not.

How to structure a results chapter

There are three defensible organising patterns, and the right one depends on how your study was designed. Choose one and apply it consistently — mixing them mid-chapter is the most common structural failure.

Pattern	Use when	Section headings look like
By research question	You have 2–4 clearly separated questions	”4.2 RQ1: How often do students use feedback tools?”
By hypothesis	Quantitative study with formal hypotheses	”4.3 H2: Engagement differs between groups”
By theme	Qualitative study where themes emerged from analysis	”4.4 Theme 2: Time pressure as a barrier”

Whichever you choose, open the chapter with a short orientation paragraph — one that states how many participants took part, what analyses follow, and in what order. Examiners use it as a map, and it costs you eighty words.

Start with the sample, not the findings

Before any result, report who and what your data actually came from: how many responses you sent and received, the response rate, how many cases you excluded and why, and any missing data. This is not padding. A reader cannot judge a mean score without knowing it came from 118 usable responses rather than 12, and burying exclusions later reads as concealment.

Follow the order you promised

If your methodology promised three hypotheses in a given order, present them in that order — do not lead with RQ3 because the finding was more exciting. Consistency across chapters is a quiet signal of rigour, and inconsistency is easy for an examiner to spot.

Reporting quantitative results

Quantitative reporting is conventional and largely formulaic — which works in your favour, because the conventions are documented and you can simply follow them.

Descriptive statistics first

Report the shape of your data before you test anything: means, standard deviations, ranges, frequencies. Give these in a table when you have more than about three numbers to convey, and in prose when you have fewer. The prose version reads like this: “Participants reported a mean engagement score of 4.21 (SD = 0.68) on a five-point scale.”

Then the test outputs

Report the test, the statistic, the degrees of freedom, the p-value, and an effect size — every time, including when the result is not significant. The APA Style guidance on reporting statistics sets out the expected format, including when to use numerals, how many decimal places to give, and how to present p-values. Follow it exactly rather than inventing your own format; markers notice.

State whether a hypothesis was supported. That sentence — “H1 was supported” — is reporting, not interpretation, because it is a factual statement about the relationship between your result and your prediction.

Table or figure?

Use a table when exact values matter, when you are presenting several variables at once, or when readers might want to extract specific numbers. Use a figure when the pattern matters more than the precise value — trends over time, distributions, comparisons between groups where the shape of the difference is the point. Never present the same data as both; choose whichever communicates faster and refer to it once.

Reporting qualitative results

Qualitative chapters give you more latitude, but the same boundary applies: describe the themes, do not yet argue what they prove.

Present themes systematically

Give each theme its own subsection with a descriptive heading, state how it was identified and how widely it appeared across participants, then present evidence. Some supervisors want frequency counts (“mentioned by 14 of 22 participants”); others consider counting antithetical to qualitative work. Ask yours, because both positions are defensible and only one is your examiner’s.

Use quotes as evidence, not decoration

Select quotes that demonstrate the theme most economically, attribute them consistently by participant code (P07, not “one student”), and keep them short enough to read. Each quote needs a sentence of framing before it and a sentence connecting it back to the theme after — a quote dropped between two paragraphs with no scaffolding makes the reader guess why it is there.

Some interpretation is unavoidable in qualitative analysis, because naming a theme is itself an analytic act. The workable line: you may explain what participants appeared to mean, but not what it implies for the field or the literature. That comparison — where your themes sit against the studies in your literature review — is discussion material.

Tables and figures that examiners accept

Formatting failures here are cheap to avoid and expensive to ignore, because they make an otherwise sound chapter look careless.

Requirement	What it means in practice
Numbering	Number sequentially by chapter (Table 4.1, Table 4.2, Figure 4.1)
Captions	Tables captioned above, figures below — a descriptive title, not “Results”
Text reference	Every table and figure must be referred to in the prose: “as Table 4.2 shows…”
Self-contained	A reader should understand it without hunting for context in the body text
Units and labels	Every axis labelled, every unit stated, every abbreviation explained in a note
Source	If reproduced or adapted from another work, cite it beneath

APA’s tables and figures guidelines cover layout, borders and notes in detail, and the accompanying sample tables are worth copying structurally. If your university publishes its own template, that template wins over any general style guide.

How much to report — and what to leave out

Report every finding your research questions promised, whether or not it flattered your hypothesis. A non-significant result is a result: “H2 was not supported, t(118) = 0.94, p = .35, d = 0.17.” Suppressing it is a research integrity problem, and examiners who spot a hypothesis that quietly disappears between the introduction and results will ask about it at the viva.

Unexpected findings get reported too — factually, in the results, with speculation about their cause saved for the discussion.

What to leave out is easier: raw software output. A pasted SPSS or R console block is not a result — it is the material you produce one from. Extract the relevant statistics into a formatted table and appendix the full output.

If you are staring at a full dataset and a blank chapter template, the Smart-Edu bachelor’s thesis generator can produce a complete structured first draft — results chapter scaffolding, tables and a matching bibliography included — in 30–90 minutes from 249 PLN. Use it the way you would use a worked example: a skeleton to populate with your own findings and interrogate line by line, never a substitute for reporting your actual data honestly.

An annotated results paragraph

Here is a short quantitative results paragraph doing its job properly:

As Table 4.2 shows, students who used the structured feedback tool reported higher engagement (M = 4.21, SD = 0.68) than students who did not (M = 3.74, SD = 0.81). An independent-samples t-test indicated that this difference was statistically significant, t(118) = 3.42, p = .001, d = 0.63. Two participants did not complete the engagement scale and were excluded from this analysis. H1 was therefore supported.

Every sentence has a specific function. The first refers the reader to the table and reports descriptive statistics for both groups. The second gives the test, statistic, degrees of freedom, p-value and effect size in the conventional order. The third discloses exclusions at the point they become relevant rather than hiding them. The fourth states the relationship to the hypothesis — a fact, not a judgement.

Notice what is absent: no explanation of why the tool worked, no comparison with other studies, no claim about what universities should do. All of that is waiting one chapter ahead. Copy the sequence — reference, descriptives, test, disclosure, hypothesis verdict — rather than the wording. And where you do cite anything in this chapter, keep the format consistent with the rest of your thesis; the conventions are covered in our guide to citing sources in academic writing.

Common mistakes in the results chapter

Mistake	Fix
Interpreting findings as you report them	Move every “this suggests…” sentence to the discussion
Dumping raw statistical output	Extract key values into a formatted table; appendix the rest
Unlabelled or uncaptioned figures	Caption everything; label every axis and unit
Reporting only supportive results	Report non-significant findings with full statistics
Reordering findings for effect	Keep the order set by your research questions
Quotes without framing	Introduce each quote and tie it back to its theme
No mention of exclusions or missing data	State response rates and exclusions up front

Tip: Before submitting, highlight every sentence in the chapter containing “because”, “suggests”, “shows that”, “indicates” or “proves”. Most of them are interpretation that has drifted in from the discussion. Move them.

Frequently asked questions about the results chapter

What is the difference between the results and the discussion chapter?

Results report what you found; the discussion explains what it means. A results sentence can be verified by anyone looking at your data — “engagement scores were higher in the intervention group, t(118) = 3.42, p = .001”. A discussion sentence involves judgement — “this suggests structured feedback supports engagement by reducing uncertainty”. If someone could reasonably disagree with your sentence while accepting your data, it belongs in the discussion.

How long should a results chapter be?

Typically 15–25% of the total word count — roughly 1,500–2,500 words in a bachelor’s thesis and 3,000–5,000 in a master’s, though tables and figures shift this considerably. Quantitative chapters are usually shorter than qualitative ones, because a table conveys in one page what quoted interview data needs several to establish. Your department’s guidelines override any general figure.

Should I report results that do not support my hypothesis?

Yes, always, with the same completeness as supportive results — statistic, degrees of freedom, p-value and effect size. A non-significant finding is a legitimate contribution, and an examiner who notices a hypothesis vanishing between your introduction and your results will treat the omission far more seriously than the null result itself.

Do I need citations in the results chapter?

Usually very few. You are reporting your own data, so there is little to cite. Legitimate exceptions: a statistical method or software package that needs referencing, a validated instrument you scored according to published rules, or a table adapted from another source. If your results chapter is dense with citations, some of it is probably literature review or discussion material that has ended up in the wrong place.

Can I include tables and raw data in the results chapter?

Formatted tables presenting your findings belong in the chapter. Raw data, full transcripts, complete statistical output and questionnaire instruments belong in appendices, referenced from the text. The rule of thumb: if a reader needs it to follow your argument, it goes in the chapter; if they might want it to verify your work, it goes in an appendix.

Summary

Knowing how to write a results chapter comes down to discipline about a single boundary: report what the data showed, in the order your research questions set, with the conventional statistics and properly formatted tables — and save every “this means” for the discussion. Start with your sample and exclusions, follow one organising pattern, report non-significant findings as fully as significant ones, and make each table and figure readable on its own. Get that right and the chapter becomes the easiest one in the thesis to write, because for once you are not arguing anything. If you are drafting the surrounding chapters too, the same principles run through our guides to writing a thesis with AI support for both bachelor’s and master’s level work.