Lesson 18 of 23 · All lessons in this module
  1. The central limit theorem
  2. The standard error of the sample meanVideo lesson
  3. Confidence intervals for the mean
  4. Value at risk: a one-sided confidence interval
  5. Populations, samples and sampling error
  6. Stratified and cluster sampling
  7. Good estimators, and the CLT across sampling designs
  8. Convenience and judgmental sampling, and choosing a method
  9. The four steps and stating the hypotheses
  10. Type I and Type II errors, significance and powerVideo lesson
  11. Critical values, p-values and the decision ruleVideo lesson
  12. p-values and p-hacking
  13. Testing a single mean
  14. Two means: independent vs paired samplesVideo lesson
  15. Testing variances: chi-square and FVideo lesson
  16. The t-test for a Pearson correlation
  17. Sample size, p-values and many correlations at once
  18. Parametric vs nonparametric tests
  19. The Spearman rank correlation testVideo lesson
  20. Contingency tables and the chi-square test of independenceVideo lesson
  21. Interpreting the result: standardized residuals and mosaics
  22. Choosing the test: four steps and a map of every test
  23. Integrated cases: testing whether a new strategy beats the old one

This lesson 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 23 · 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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