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Interpreting Statistical Output for Dissertations, Essays and Assignments: Writing Clear Results

Chris

Clear, accurate reporting of statistical results is essential for dissertations, essays and assignments. Readers — examiners, supervisors and peers — need to understand what you found, how you found it, and what the numbers mean for your research question. This guide shows how to interpret common outputs, structure the Results section, and write concise, trustworthy statements that follow best practice.

Why clear statistical reporting matters

  • Reproducibility: Clear reporting lets others reproduce your findings and verify conclusions.
  • Credibility: Transparent presentation of methods, assumptions and effect sizes supports trustworthiness.
  • Readability: Examiners often skim Results; concise tables and focused sentences save time and reduce misinterpretation.

For guidance on selecting appropriate tests before you run analyses, see Selecting the Right Statistical Tests for Dissertations, Essays and Assignments: A Practical Decision Tree.

Preparing to interpret outputs

Before interpreting:

Interpreting common outputs

Below are concise rules and example templates for the most common tests.

Descriptive statistics

What to report:

  • Sample size (n), mean (M), standard deviation (SD), median/IQR for skewed data.
  • Percentages for categorical variables.

Example sentence templates:

  • “The sample (n = 120) had a mean age of M = 34.5 years (SD = 8.2).”
  • “Most participants identified as female (62%, n = 74).”

t-tests (independent & paired)

Key outputs: t-value, degrees of freedom (df), p-value, effect size (Cohen’s d), CI for mean difference.

  • Report df as shown by software (e.g., Welch’s df if unequal variances).
  • Prefer effect size and CI alongside p-value.

Template:

  • “An independent-samples t-test indicated that Group A scored higher (M = 4.1, SD = 0.9) than Group B (M = 3.4, SD = 1.0), t(98) = 3.02, p = .003, d = 0.60, 95% CI [0.21, 1.20].”

ANOVA

Key outputs: F, df (between, within), p-value, partial eta-squared (ηp²), post-hoc test results.

  • If ANOVA is significant, report which pairwise differences are significant using corrected p-values (e.g., Bonferroni).

Template:

  • “A one-way ANOVA revealed a significant effect of condition on score, F(2, 147) = 5.67, p = .004, ηp² = .07. Post-hoc tests (Bonferroni) showed Condition 1 differed from Condition 3 (p = .002).”

Regression (linear)

Key outputs: regression coefficients (B), standard error (SE), t-value, p-value, R², adjusted R², CI for coefficients.

  • Report unstandardized coefficients unless you explicitly compare predictors measured on different scales (then report β — standardized coefficients).
  • Discuss model fit (R²) and practical significance.

Template:

  • “Multiple linear regression indicated that X significantly predicted Y (B = 0.45, SE = 0.12, t = 3.75, p < .001), controlling for age and gender. The model explained 28% of variance in Y, R² = .28, adjusted R² = .25.”

Chi-square tests

Key outputs: χ², df, p-value, expected frequencies, effect size (Cramér’s V).

  • Ensure expected counts meet test assumptions (no more than 20% expected counts <5).

Template:

  • “There was a significant association between gender and preference, χ²(1, N = 210) = 6.34, p = .012, Cramér’s V = .17.”

Reporting templates: What to include in the Results section

Use a simple, consistent structure. Below is a recommended template table you can adapt.

Element What to include Example phrase
Opening Brief reminder of analysis purpose “To test H1, we compared X across groups.”
Test & assumptions Name of test and assumption checks “Assumptions of normality were examined using Shapiro–Wilk (p = .12).”
Main result Test statistic, df, p, effect size, CI “t(98) = 3.02, p = .003, d = 0.60, 95% CI [0.21, 1.20].”
Secondary results Post-hoc or sensitivity analyses “A sensitivity analysis excluding outliers produced similar results.”
Short interpretation One sentence summarizing result (no broader inference) “These results indicate a statistically significant difference between groups.”

Visuals and tables: best practices

Assumptions, missing data and robustness

Common mistakes to avoid

  • Relying on p-values alone — always report effect sizes and CIs.
  • Over-interpreting Results — reserve broader interpretation for the Discussion.
  • Omitting sample sizes or df — these allow readers to evaluate precision.
  • Mixing Results and Methods — keep the Results section focused on findings.

Quick checklist for writing Results

  • State which analysis tested each hypothesis.
  • Report n, M, SD (or median, IQR).
  • Include test statistic, df, p, effect size, and CI.
  • Provide concise interpretation in past tense.
  • Add clear tables/figures with captions.
  • Mention assumption checks and data handling.
  • Link to supplementary material or code if available (reproducibility).

For reproducible workflows and sharing code, consult Reproducible Analysis Workflows for Dissertations and Assignments Using R and Python.

Further reading (related topics)

Contact us — help with writing or proofreading

If you need professional assistance with writing, interpreting outputs, formatting results, or proofreading your dissertation, essay or assignment, contact MzansiWriters:

  • Use the WhatsApp icon on the page for quick enquiries.
  • Email: info@mzansiwriters.co.za
  • Or visit the Contact Us page via the main menu.

Good statistical reporting strengthens your argument — present it clearly, precisely and transparently.