Selecting the Right Statistical Tests for Dissertations, Essays and Assignments: A Practical Decision Tree
Choosing the correct statistical test is essential for credible, reproducible results in dissertations, essays and assignments. This practical guide gives a clear decision tree, test mappings, examples and reporting tips so you can pick (and justify) the right method with confidence.
Why the right test matters
- Accuracy: Wrong tests produce misleading p-values and conclusions.
- Credibility: Examiners expect appropriate methods and clear reasoning.
- Reproducibility: Appropriate tests with assumptions checked make your work defensible.
Below is a concise decision tree you can follow, plus tables and examples to help you decide quickly.
Quick decision tree (step-by-step)
- Identify your primary research question — comparing groups, testing association, predicting an outcome, or describing distribution?
- Determine variable types — is the outcome variable continuous, ordinal, or categorical? Are predictor(s) continuous or categorical?
- Count groups / relationships — two groups vs more than two; independent vs paired; single variable correlations; multiple predictors (regression).
- Check assumptions — normality, homogeneity of variances, independence, linearity (for regression). If assumptions fail, choose a nonparametric or robust alternative.
- Select test & report — state test, assumptions checked, effect sizes, confidence intervals and exact p-values.
Step 1 — What is your outcome (dependent) variable?
- Continuous (interval/ratio): e.g., test score, blood pressure.
- Ordinal: e.g., Likert scale categories with order.
- Categorical (nominal / binary): e.g., gender, pass/fail.
Step 2 — How many groups / predictors?
- Two groups: independent or paired?
- More than two groups: between-subjects or repeated measures?
- Association/Prediction: correlation or regression (simple vs multiple, linear vs logistic).
Common tests and when to use them
| Goal | Outcome type | Typical test | Nonparametric/alternative | When to use |
|---|---|---|---|---|
| Compare two independent group means | Continuous | Independent t-test | Mann–Whitney U | Use t-test if normality and equal variances hold; else Mann–Whitney |
| Compare two paired means | Continuous, paired | Paired t-test | Wilcoxon signed-rank | Pre/post designs or matched pairs |
| Compare >2 group means | Continuous | One-way ANOVA | Kruskal–Wallis | Follow with post-hoc tests (Tukey). If violated, use Kruskal–Wallis |
| Compare proportions | Categorical | Chi-square test (χ²) | Fisher’s Exact (small counts) | Use Fisher’s when expected counts <5 |
| Association between two continuous variables | Continuous | Pearson correlation | Spearman rank | Use Spearman when variables non-normal or ordinal |
| Predict continuous outcome from predictors | Continuous | Linear regression | Robust regression / transform variables | Check linearity, residuals normality, homoscedasticity |
| Predict binary outcome | Binary outcome | Logistic regression | Penalized logistic (small samples) | Use for odds ratios and adjusted predictors |
| Compare >2 repeated measures | Continuous repeated | Repeated measures ANOVA | Friedman test | Check sphericity (Mauchly’s test); use Greenhouse-Geisser correction if violated |
Correlation vs Regression — quick distinction
| Question | Use |
|---|---|
| Do you want to describe association (strength/direction)? | Correlation (Pearson/Spearman) |
| Do you want to predict an outcome or adjust for other variables? | Regression (linear, logistic, Poisson, etc.) |
Practical examples
-
Example 1 — “Do male and female students differ in mean exam score?”
- Two independent groups, continuous outcome → Independent t-test (or Mann–Whitney U if non-normal).
-
Example 2 — “Is smoking status associated with hypertension (yes/no)?”
- Two categorical variables → Chi-square test (or Fisher’s Exact if small cell counts).
-
Example 3 — “Does study time predict exam score when controlling for prior GPA?”
- Continuous outcome, multiple predictors → Multiple linear regression.
-
Example 4 — “Does an intervention change pain scores before vs after?”
- Paired continuous → Paired t-test (or Wilcoxon signed-rank).
Assumptions checklist (always report these)
- Normality of residuals or outcome (graphical: histograms/Q-Q plots; tests: Shapiro–Wilk).
- Homogeneity of variance (Levene’s test for t/ANOVA).
- Independence of observations (study design).
- Linearity and absence of multicollinearity (for regression).
- Sphericity for repeated measures (Mauchly’s test).
If assumptions fail, report what you did: transformation, nonparametric test, robust estimator, or bootstrapping.
Reporting essentials for dissertations and assignments
- State the test name and why it was chosen.
- Report test statistic, degrees of freedom, p-value, effect size and 95% CI.
- Show assumption checks and any remedial steps.
- Include tables/figures that summarise results clearly.
For guidance on writing results sections and interpreting output, see Interpreting Statistical Output for Dissertations, Essays and Assignments: Writing Clear Results.
Additional resources from the Data Analysis & Statistics for Theses pillar
- Regression, ANOVA and Beyond: Applied Statistics for Dissertations, Essays and Assignments — deeper regression and ANOVA guidance.
- Handling Missing Data and Outliers in Dissertations, Essays and Assignments: Strategies and Examples — how to treat problematic data.
- Power Analysis and Sample Size Planning for Dissertation and Assignment Studies — plan sample size before analysis.
- Data Visualization Best Practices for Dissertations, Essays and Assignments: Charts, Tables and Figures That Communicate — display results clearly.
- Reproducible Analysis Workflows for Dissertations, Essays and Assignments Using R and Python — scripts, notebooks and version control.
- Beginner’s Guide to Qualitative Coding and Thematic Analysis for Dissertations, Essays and Assignments — if your project includes qualitative data.
- Mixed-Methods Data Integration: Techniques for Dissertations, Essays and Assignments — combining qualitative and quantitative findings.
- Qualitative Trustworthiness and Quantitative Validity: Reporting Standards for Dissertations, Essays and Assignments — how to make results credible.
Quick reference table (compact)
| Scenario | Recommended test | Alternative |
|---|---|---|
| Two independent group means | Independent t-test | Mann–Whitney U |
| Two paired measurements | Paired t-test | Wilcoxon signed-rank |
| >2 group means | One-way ANOVA | Kruskal–Wallis |
| Two categorical variables | Chi-square | Fisher’s Exact |
| Two continuous variables association | Pearson r | Spearman rho |
| Continuous outcome with predictors | Linear regression | Transform / robust methods |
| Binary outcome with predictors | Logistic regression | Penalized logistic (small n) |
Final tips
- Pre-plan analysis (use Power Analysis and Sample Size Planning for Dissertation and Assignment Studies).
- Check and handle missing data and outliers (see Handling Missing Data and Outliers…).
- Keep analysis reproducible and well-documented (Reproducible Analysis Workflows…).
- Use clear visuals for results (Data Visualization Best Practices…).
- When using mixed methods or qualitative data, integrate thoughtfully (Mixed-Methods Data Integration… and Beginner’s Guide to Qualitative Coding and Thematic Analysis…).
Need writing or proofreading help?
If you’d like professional help with analysis sections, interpretation or proofreading your dissertation, essay or assignment, contact MzansiWriters:
- Click the WhatsApp icon on our page,
- Email: info@mzansiwriters.co.za,
- Or use the Contact Us page accessed via the main menu.
Good luck — choose your test deliberately, check assumptions, and report clearly.