How to Write a Results Section for a Research Paper: Complete Guide

You’ve done the analysis. You’ve got the data. You just need to present what you found — without jumping into interpretation, explanation, or comparison to previous research. That’s exactly what the results section is for.

Writing a results section is one of the most common pain points for students. You know your data tells a story, but academic writing demands you report only what happened — nothing more, nothing less. The difference between a strong results section and a weak one often determines whether your paper gets accepted, graded well, or ends up with comments like “save interpretation for the discussion.”

This guide shows you exactly how to structure, write, and format a results section across disciplines — with worked examples from STEM, social sciences, and humanities, plus downloadable templates you can use today.

What Is the Results Section?

The results section (sometimes called “Findings”) is the part of your research paper where you objectively report your study’s key findings — without interpreting them, comparing them to previous research, or explaining why they happened.

Think of it like this: your results section answers “what did you find?” Your discussion section answers “what does it mean?”

Every results section has three core components:

1. Descriptive statistics — summarizing your data (means, standard deviations, frequencies)
2. Inferential statistics — testing hypotheses (t-tests, ANOVA, regression)
3. Visual references — pointing readers to tables, figures, or charts that illustrate your findings

And critically: it reports results in past tense, because you’re describing work that’s already been completed.

Results vs. Discussion: What’s the Difference?

This is the single most common mistake students make — and it’s one of the reasons this guide exists.

According to San Jose State University’s Writing Center writing guide on research papers, “The results section simply reports your results. The discussion section interprets those results, discusses what they mean, and places them in the context of existing research.” This distinction exists because peer reviewers need to see the raw findings separated from the author’s interpretation — otherwise, they can’t tell which claims are supported by data and which are speculative.

The bottom line: If you’re tempted to write “This means that…” or “These findings show that…”, stop. That belongs in the discussion section.

Writing a Results Section: The 4-Step Formula

Every results section, regardless of discipline, follows the same four-step logic. You’ll organize your findings, report each finding with supporting data, reference visuals, and include every relevant result — including null findings.

Step 1: State the Finding

Start every results paragraph with the finding itself. Never start with the test name or the methodology.

Good:

There was a statistically significant difference in test scores between the experimental and control groups.

Bad:

A t-test was conducted to evaluate whether there was a difference between the experimental and control groups.

The APA 7th Edition Numbers and Statistics Guide recommends leading with the substantive finding, then immediately following with the test statistic, p-value, and effect size. Students who lead with statistical methods confuse readers about whether they’re in the methodology or results section.

Step 2: Provide Data & Statistics

Every finding you report needs supporting numbers. If you’re describing a finding, include:

• The measure of central tendency (mean, median, or mode)
• The measure of variability (standard deviation, range, or interquartile range)
• The sample size (N)

Example:

Participants in the intervention group reported significantly higher satisfaction scores (M = 4.2, SD = 0.8, n = 45) than the control group (M = 3.1, SD = 1.1, n = 42).

Notice how every finding has descriptive statistics embedded in the sentence. This is the default structure across quantitative disciplines.

Step 3: Reference Visuals (Tables/Figures)

Don’t bury your best data in paragraphs. Point readers to tables and figures where they can see the full picture.

Every table and figure in a results section should:

• Have a concise, descriptive title
• Be referenced directly in the text (“As shown in Table 1…”)
• Not duplicate information that’s already reported in the text

Example:

The paired-samples t-test revealed a significant increase in test scores from pre-intervention (M = 62.4, SD = 8.3) to post-intervention (M = 71.8, SD = 7.1), t(44) = 3.45, p = .001, d = 0.53. As shown in Figure 1, this increase was consistent across all demographic groups.

Notice the text reports the statistics and references the figure. You’re doing both.

Step 4: Include Null Results

This is where most students lose credibility — and where you can gain it.

Null results (non-significant findings) are not failures. They’re data. Reporting them honestly signals academic integrity and strengthens your paper’s overall credibility. According to research from the Nature Index, non-significant results are difficult to publish in journals, which means many researchers choose not to submit them — but as a student, you have an obligation to report everything.

How to report a null result:

There was no statistically significant difference between the intervention and control groups in self-reported stress levels, t(44) = 1.23, p = .22.

That’s it. One sentence. Report the test, the p-value, and state clearly that there was no significant difference. You don’t need to explain why it wasn’t significant — that belongs in the discussion section.

According to the Journal of Experimental Psychology, “adequate reporting and follow-up of non-significant test results is a methodological requirement that prevents confirmation bias from shaping the literature.” Skipping null results is one of the most common reasons peer reviewers flag a paper.

Quantitative Results Writing

If your paper is quantitative — that means you worked with numerical data, statistical tests, and measurable outcomes — your results section follows a specific structure.

Statistical Reporting

Every statistical test needs a standard format. The APA 7th Edition statistics guide specifies:

1. Test name in italics (e.g., t-test, ANOVA, regression)
2. Degrees of freedom in parentheses
3. Test statistic (e.g., t = 3.45)
4. Exact p-value (unless p < .001, report as “p < .001”)
5. Effect size (Cohen’s d, η², R²)
6. 95% confidence interval (when appropriate)

Example format:

There was a statistically significant difference between groups, F(2, 128) = 5.67, p = .003, η² = .083, 95% CI [0.031, 0.152].

APA Format for Statistics

APA 7th Edition has very specific rules for statistics formatting. Here are the most common patterns students get wrong:

• Italicize statistical symbols (p, M, SD, t, F, r, β)
• Do not italic sample sizes (n = 45)
• Report exact p-values (e.g., p = .015), except when p < .001
• Use non-directional language even when you predicted a direction
• Report effect sizes for every inferential statistic

According to the official APA 7th Edition Numbers and Statistics Guide, “In tables and figures, report exact p values (e.g., p = .015), unless p is < .001 (instead write as ‘p < .001’).” This exact formatting rule is tested regularly in academic writing courses, and missing it is one of the most common grading deductions.

Reporting Non-Significant Results

Don’t skip them. Report them the same way you report significant ones — test name, statistic, degrees of freedom, p-value, effect size.

Example:

The correlation between study time and exam performance was not statistically significant, r(98) = 0.12, p = .20, 95% CI [−0.06, 0.29].

The 95% confidence interval here crosses zero, which tells the reader this result is not statistically distinguishable from no effect. Reporting the CI alongside the p-value adds analytical depth that professors notice.

Qualitative Results Writing

Qualitative results look very different from quantitative ones. Instead of tables of numbers and statistical tests, you work with themes, quotes, and narrative descriptions.

Thematic Analysis Presentation

The most common qualitative structure is thematic analysis — you identify themes in your data and present each theme as a separate section. Each theme gets:

• A descriptive heading
• 2–4 verbatim quotes from participants
• Brief analytical commentary explaining how the quote supports the theme

Example structure:

Theme 1: Financial barriers limit participation
Participants consistently described cost as a primary barrier to engaging with clinical trials. As one participant stated, “I want to participate, but the travel costs and time off work make it impossible for me to attend every appointment.” Another noted, “The financial burden of participating is real — you’re spending your own money just to be part of the study.”

Notice the pattern: theme heading, participant quote, analytical follow-up. This is the standard format recommended by the Sage Publishing guide to qualitative interview data and the Braun & Clarke reflexive thematic analysis framework.

Representative Quotes

Every quote in a qualitative results section should:

• Be anonymized (no names or identifying details)
• Be directly relevant to the theme
• Be preceded by the theme heading
• Be followed by brief commentary (1–3 sentences)

Interview Data Reporting

When reporting interview data, you may also include:

• Frequency counts (e.g., “12 of 15 participants reported…”)
• Demographic breakdowns (e.g., “Of the 8 female participants, 6 described…”)
• Contrast quotes (e.g., “While most participants emphasized X, two participants noted Y…”)

The University of Manchester’s guidance on qualitative data analysis recommends that “the results chapter should present the themes and findings clearly and objectively, using participant quotes to illustrate and substantiate each theme.”

Structure and Organization

How you organize your results section depends on your methodology and discipline. There’s no single “right” way, but there are three common patterns.

Chronological Order

Present findings in the order they occurred or were collected. This works well when your study had multiple phases or when the sequence of findings matters for interpretation.

Use when: Your study had clear phases (pretest, intervention, posttest) or when the temporal sequence adds clarity.

Example:

First, we examined baseline differences between groups. Then, we analyzed post-intervention outcomes. Finally, we tested for follow-up sustainability.

Method-Based Organization

Organize by your methodology or data collection approach. This works well for mixed-methods studies or when different methods produced distinct types of findings.

Use when: Your study used multiple data collection methods (surveys, interviews, observations) that produced different kinds of results.

Example:

Survey results indicated X. Interview findings revealed Y. Observational data showed Z.

Theme-Based Layout (Qualitative)

Organize by themes or concepts. This is the standard for qualitative research and works well when findings cluster around identifiable categories.

Use when: Your data analysis produced clear themes, categories, or clusters.

Example:

Theme 1: X
Theme 2: Y
Theme 3: Z

Each theme gets its own subsection with headings. This structure makes the paper scannable and signals organized thinking to reviewers.

Common Mistakes

Avoiding these four mistakes will immediately improve any results section:

1. Over-Interpreting Results

This is the most common error and the one that costs the most points. If you’re explaining why you got a result, comparing your results to previous studies, or discussing implications, you’ve crossed into discussion territory.

Fix: Delete any sentence that starts with “This suggests,” “This means,” or “These findings indicate.” Move those sentences to the discussion section.

2. Including Raw Data

Don’t paste your entire dataset into the results section. Report aggregated, summarized findings — not raw numbers.

Fix: Use descriptive statistics (means, frequencies, percentages) and inferential statistics (tests, confidence intervals) instead of raw observations.

3. Repetitive Descriptions

If you report the same finding in a table and then describe it verbatim in the text, you’re wasting words and confusing readers.

Fix: Use tables and figures to show the full data. Use text to highlight the most important patterns or test statistics. Don’t repeat both.

4. Leaving Out Null Results

As discussed above, non-significant findings are legitimate results. Skipping them creates confirmation bias in your paper and signals incomplete analysis to reviewers.

Fix: Report every result — significant, non-significant, unexpected, or contradictory — in the order your analysis produced them.

Worked Examples

Let’s walk through three discipline-specific examples so you can see exactly how this looks in practice.

Example 1: STEM (Quantitative — ANOVA Results)

A one-way between-groups ANOVA was conducted to determine whether there were differences in laboratory performance scores across three teaching methods (traditional lecture, flipped classroom, and hands-on lab). There was a statistically significant difference in scores between at least two of the groups, F(2, 147) = 8.92, p < .001, η² = .108. Post-hoc comparisons using Tukey’s HSD test revealed that the hands-on lab group scored significantly higher than the traditional lecture group (M = 84.2, SD = 5.1) and the flipped classroom group (M = 79.6, SD = 6.3). As shown in Figure 2, the difference was most pronounced in the hands-on lab group, where practical application appeared to reinforce theoretical concepts. There was no significant difference between the traditional lecture and flipped classroom groups, p = .34.

Notice: test name, degrees of freedom, exact statistics, effect size, visual reference, and a null result — all in one coherent paragraph. This is how a strong STEM results section looks.

Example 2: Social Sciences (Qualitative — Thematic Results)

Analysis of the 45 participant interviews revealed three distinct themes regarding student motivation.
Theme 1: External pressure as primary motivator
Most participants identified grade requirements and parental expectations as their primary drivers. As one participant stated, “I’m not particularly interested in the subject, but I need the grade. My parents expect me to maintain a 3.5 GPA.” This pattern was consistent across demographic groups, with 89% of participants citing academic pressure as their main motivator.
Theme 2: Intrinsic curiosity emerges over time
Several participants described a shift from extrinsic to intrinsic motivation during their coursework. “Initially, I just wanted the grade, but then I started enjoying the actual work. That’s when things changed.” This transition was particularly common among students in their final year of study.
Theme 3: Financial concerns create stress
A minority of participants (n = 12) reported that financial stress interfered with their academic performance. “I’m working 20 hours a week. By the time I sit down to study, I’m exhausted.” These students showed lower engagement scores across all measured dimensions.

This example demonstrates the standard qualitative structure: theme heading, representative quote, supporting data (percentage/frequency count), and brief analytical follow-up.

Example 3: Humanities (Textual Analysis Results)

Close reading of the selected primary texts revealed three recurring patterns of narrative voice. First, all three novels use a third-person limited perspective that restricts reader access to a single character’s consciousness. Second, dialogue in all texts contains a consistent pattern of interrupted speech, where characters frequently cut each other off mid-sentence. This pattern appears in 47 instances across the three texts, suggesting a deliberate stylistic choice rather than accidental dialogue. Third, metaphorical language shifts systematically between the first and third sections of each novel — early sections favor agricultural metaphors, while later sections shift to architectural imagery.

This humanities example shows how textual analysis results are reported: pattern identification, supporting evidence (frequency count), and disciplinary interpretation without crossing into discussion.

Quick Checklist

Before you submit your results section, verify each item:

• [ ] Does every paragraph start with the finding, not the method?
• [ ] Are all statistics formatted in APA 7th Edition style (italics, exact p-values, effect sizes)?
• [ ] Is the section entirely in past tense?
• [ ] Are tables and figures referenced directly in the text?
• [ ] Are null results included alongside significant findings?
• [ ] Is interpretation, comparison, or explanation absent? (That belongs in the discussion.)
• [ ] Are raw datasets replaced with summarized statistics?
• [ ] Is the organization logical (chronological, thematic, or method-based)?

If you can check all eight items, your results section is structurally sound.

Templates and Downloads

Download our Results Section Template — a fill-in-the-blank worksheet that maps the 4-step formula (find, report, reference, include null) to your specific paper. It includes APA statistics format reminders and discipline-specific tips.

Download the template and start building your results section today.

Related Guides

• Research Paper Outline Template — Step-by-step structure for your entire paper
• How to Write a Discussion Section — What comes after results
• Literature Review Guide — How to prepare for the results section
• How to Write a Problem Statement — Defining your research question

Need Help Getting Started?

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Writing a results section is straightforward when you follow the four-step formula: state the finding, provide data, reference visuals, and include every result (even null findings). The most common mistake is interpreting results — if you’re explaining why something happened or comparing it to previous research, stop. That’s discussion territory.

Format your statistics using APA 7th Edition rules (italicize test names and symbols, report exact p-values, include effect sizes). Organize your findings logically — chronologically, thematically, or by method — depending on what makes the most sense for your study.

And remember: null results are data. Report them honestly, and your paper will look more rigorous than most.

Your next step: download the results section template, walk through the four steps with your data, and write your section without crossing into interpretation. If you need support, we’re here to help.