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Effect sizes for nonparametric tests
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DOI：10.11613/BM.2026.010101.png)
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Abstract
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Effect size measures are important complements to P values, providing information about the magnitude and practical relevance of research findings. While widely discussed in the context of parametric tests, effect size estimation for nonparametric tests remains less explored. This article reviews standardized effect size measures applicable to four common nonparametric tests: Mann-Whitney, Wilcoxon signed-rank, Kruskal-Wallis, and Friedman. Commonly suggested classifications for these effect sizes are also discussed. This article aims to support researchers in reporting and interpreting effect sizes more effectively in nonparametric contexts.
Keywords:
biostatistics
effect size
nonparametric statistics
writing in science
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1.8
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601
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5.3K
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