Should you use a t-test, chi-square, or ANOVA? This decision guide helps you select the appropriate statistical test based on your research question, data type, and sample size.
Why Choosing the Right Test Matters
Selecting the wrong statistical test is one of the most common and consequential errors in research. Using a parametric test on non-normal data, or analysing paired data with an unpaired test, produces incorrect p-values and confidence intervals — undermining your conclusions. The right test depends on three things: your research question, the type of data you have collected, and whether your data meet the assumptions of the chosen test.
Step 1 — Identify Your Variable Types
Start by classifying your outcome variable. Continuous variables (blood pressure, BMI, HbA1c) have a meaningful range of values. Categorical variables are either nominal (blood group, religion) or ordinal (Likert scale, disease severity grades). Dichotomous variables are binary outcomes (disease: yes/no). Your outcome type is the primary driver of test selection.
Comparing Two Independent Groups
If your outcome is continuous and normally distributed, use the independent samples t-test. Check normality with the Shapiro-Wilk test or inspect a Q-Q plot. If normality is violated, use the Mann-Whitney U test (non-parametric equivalent). For a categorical outcome, use the Pearson chi-square test (or Fisher's exact test if any expected cell count is below 5). For an ordinal outcome, use the Mann-Whitney U test.
Comparing Two Related (Paired) Groups
When comparing measurements from the same participants at two time points (pre- and post-intervention), use the paired t-test for normally distributed continuous data or the Wilcoxon signed-rank test for non-normal data. For paired dichotomous outcomes (e.g., whether the same person developed the outcome before vs. after an intervention), use McNemar's test.
Comparing Three or More Groups
For three or more independent groups with a continuous, normally distributed outcome, use one-way ANOVA. If ANOVA shows a significant overall difference, apply a post-hoc test (Tukey HSD or Bonferroni) to identify which groups differ. If normality assumptions are violated, use the Kruskal-Wallis test with Dunn's post-hoc correction. For a categorical outcome across three or more groups, use the chi-square test.
Assessing Relationships and Associations
To measure the relationship between two continuous variables, use Pearson's correlation (if both are normally distributed) or Spearman's rank correlation (if non-normal or ordinal). To examine the association between an exposure and a binary outcome while controlling for confounders, use logistic regression. For continuous outcomes with multiple predictors, use linear regression. For time-to-event outcomes, use Kaplan-Meier curves and Cox proportional hazards regression.
A Quick Reference Guide
Continuous outcome, 2 groups, independent: t-test or Mann-Whitney U. Continuous outcome, 2 groups, paired: paired t-test or Wilcoxon. Continuous outcome, 3+ groups: ANOVA or Kruskal-Wallis. Categorical outcome, 2+ groups: chi-square or Fisher's exact. Correlation between 2 continuous variables: Pearson or Spearman. Binary outcome with confounders: logistic regression. This framework covers the vast majority of analyses in clinical and public health research.
Conclusion
Selecting the right statistical test is a skill that improves with practice. When in doubt, draw a simple diagram of your study design, list your variable types, and work through the decision tree above. Research Sathi's biostatistics team can help you choose the appropriate analysis, run it in SPSS or R, and interpret and present your results correctly.
Mr. Bikram Karki
Biostatistician — Research Sathi
Our team brings together experienced researchers, statisticians, and academic writers dedicated to helping students and professionals across Nepal produce impactful research.