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Nonparametric Methods

StatisticsNonparametric🟒 Free Lesson

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Nonparametric Methods


Overview

Nonparametric methods replace distributional assumptions with rank-based or permutation-based approaches. The Mann-Whitney U test is the nonparametric alternative to the two-sample t-test, comparing distributions using ranks. The Wilcoxon signed-rank test replaces the paired t-test for matched data. The Kruskal-Wallis test extends to multiple groups (nonparametric ANOVA). The Friedman test handles repeated measures. Permutation tests build exact null distributions by shuffling labels, providing p-values for any test statistic. Bootstrap methods approximate sampling distributions without assumptions. These tests are slightly less powerful than parametric tests when assumptions hold, but far more robust when they don't.


Key Concepts

Nonparametric Test Equivalents

Nonparametric TestParametric EquivalentUse CaseWhat It Tests
Mann-Whitney UTwo-sample t-testIndependent samplesDistributions differ
Wilcoxon signed-rankPaired t-testPaired samplesMedian difference β‰  0
Kruskal-WallisOne-way ANOVAMultiple independent groupsAt least one group differs
FriedmanRepeated-measures ANOVAMultiple related groupsTreatment effects
Permutation testAny parametric testAny test statisticAny null hypothesis

When to Use Each Test

Data CharacteristicRecommended Test
Skewed, 2 independent groupsMann-Whitney U
Skewed, paired dataWilcoxon signed-rank
Skewed, 3+ independent groupsKruskal-Wallis
Ordinal dataAny rank-based test
Very small sample ()Permutation test
Complex test statisticBootstrap

Quick Example


Key Takeaways


Deep Dive

For detailed explanations, worked examples, and Python implementations, explore the dedicated statistics lessons:

Permutation Tests

  • Permutation Tests β€” Exact p-values by shuffling labels, no distributional assumptions, and when to use

Paired Tests

Independent Group Tests

Multiple Group Tests

Repeated Measures

  • Friedman Test β€” Nonparametric repeated-measures ANOVA for related groups

Related Topics

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