This module is part of the 2027 curriculum. You are following the 2026 curriculum, where it is not taught in this form. Switch if you are sitting the exam under the 2027 curriculum.
Lesson 18 of 22 · 12 min
Parametric vs nonparametric tests
Parametric tests are about parameters and lean on distribution assumptions; when those assumptions fail, the data are ranks, or the question is not about a parameter, use a nonparametric test.
In short
- A parametric test concerns a population parameter (mean, variance) and relies on specific distributional assumptions, usually normality.
- A nonparametric test is not concerned with a parameter, or makes only minimal assumptions about the population.
- Four reasons to go nonparametric: assumptions not met, outliers, data given as ranks (ordinal scale), or a hypothesis that is not about a parameter.
- Alternatives: single mean → Wilcoxon signed-rank; paired differences → Wilcoxon signed-rank or sign test; two independent means → Mann–Whitney U (Wilcoxon rank sum).
- When the parametric assumptions hold, the parametric test is preferred because it usually has more power.
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