Choosing the Right Statistical Test for Your Data

Choosing a statistical test starts with two questions: what type of data do you have, and what are you trying to compare or relate? For comparing means between two groups, use a t-test if data are roughly normal, or a Mann-Whitney U test if not. For more than two groups, use ANOVA (or Kruskal-Wallis for non-normal data). For relationships between two continuous variables, use correlation or regression. For categorical data, use chi-square tests of independence. For repeated measures on the same subjects, use paired tests or repeated-measures ANOVA rather than treating observations as independent. Always check your test's assumptions — normality, homogeneity of variance, independence of observations — before running it, and report which assumptions you checked and how. Reviewers in quantitative fields will often ask for this explicitly if it's missing from your methods section.