Picking a test is where most thesis stats panic starts — not APA formatting. This is a plain decision path for the most common designs in social science/health/education research:
- Comparing two groups? → independent samples → independent t-test (normal) or Mann-Whitney U (non-normal/ordinal). Same subjects measured twice → paired t-test or Wilcoxon signed-rank.
- Comparing 3+ groups? → one-way ANOVA (normal, equal variance) or Kruskal-Wallis H (non-normal). Two categorical factors at once → two-way ANOVA.
- Relationship between two continuous variables? → correlation (Pearson if linear+normal, Spearman if not) or simple regression if you need prediction, not just association.
- Categorical vs categorical? → chi-square test of independence (or Fisher's exact if any expected cell count < 5).
- Scale reliability before you even run the above? → Cronbach's alpha first — a test result built on an unreliable scale isn't worth reporting.
The underlying question is always: what's the outcome variable's type (continuous/ordinal/categorical), how many groups, and are your normality/variance assumptions met? Get those three answers and the test picks itself.
If you want to walk through this interactively with your own data, StatMate has a free decision-tree wizard (statmate.org/wizard) that asks these same questions and routes you to the right calculator — no signup, and it runs 20 tests including the ones above.
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