How to Choose the Right Statistical Test

Choosing a statistical test starts with the research question and data structure. This guide helps narrow the options before running an analysis.

Start with the research question

Decide whether the goal is to describe a sample, compare groups, examine an association, or predict an outcome. The objective determines the family of analyses to consider.

Identify variable types

Nominal variables represent unordered categories, ordinal variables represent ranked categories, and quantitative variables contain numeric values for which means and standard deviations may be meaningful.

Count groups and check dependence

An independent-samples t test may compare two separate groups when assumptions hold. Paired methods apply when the same participants are measured twice. ANOVA may suit comparisons involving three or more groups.

Check assumptions

Review distributions, outliers, variance equality, and sample size. If assumptions are not reasonable, a transformation or a nonparametric method such as Mann–Whitney or Kruskal–Wallis may be more suitable.

Go beyond the p-value

Report effect size and confidence intervals with the p-value, then connect the finding to the research question. Statistical significance alone does not establish practical importance.

Need help analyzing your data?

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