Effect sizes for nonparametric tests

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.

Authors

Publication Details

Journal
Biochemia Medica
Published
2025-12-12
DOI
https://doi.org/10.11613/bm.2026.010101
Citations
7
Primary Topic
Meta-analysis and systematic reviews
Type
article
Field-Weighted Citation Impact
9.79
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article

Effect sizes for nonparametric tests

Fernanda Fiel Peres
7 citations
Biochemia Medica
Meta-analysis and systematic reviews
9.79
article

Effect sizes for nonparametric tests

Fernanda Fiel Peres
article en
7 citations

Abstract

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.

Biochemia MedicaVol. 36(1)
Openalex Percentile: Top 2%
Meta-analysis and systematic reviews
9.79
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