Biochemia Medica· 2025Q2
Effect sizes for nonparametric tests
- 173citations
- Q2SCImago
- 2025year
Short summary
This article reviews standardized effect size measures for four common nonparametric tests (Mann-Whitney, Wilcoxon signed-rank, Kruskal-Wallis, Friedman), aiming to improve their reporting and interpretation.
AI-generated from the title and abstract; the full text is not read.
Key points
- Effect sizes complement P values by quantifying the magnitude of research findings.
- Standardized effect size measures are reviewed for Mann-Whitney, Wilcoxon signed-rank, Kruskal-Wallis, and Friedman tests.
- Common classifications for interpreting these effect sizes are discussed.
- The article aims to support researchers in reporting and interpreting effect sizes in nonparametric contexts.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Biochemia Medica, 2025 · DOI ↗
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Statistics, Probability and UncertaintyDecision Sciences