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Effect sizes for nonparametric tests.

Fernanda Fiel Peres1

  • 1Independent researcher, São Paulo, Brazil.

Biochemia Medica
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

Effect size measures quantify research findings beyond P values. This review details standardized effect sizes for common nonparametric tests, aiding interpretation in statistical analysis.

Keywords:
biostatisticseffect sizenonparametric statisticswriting in science

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Biostatistics

Background:

  • P values are insufficient for interpreting research findings.
  • Effect size measures are crucial for understanding the magnitude of effects.
  • Effect size estimation for nonparametric tests is less explored than for parametric tests.

Purpose of the Study:

  • To review standardized effect size measures for common nonparametric tests.
  • To discuss classifications for these effect sizes.
  • To enhance the reporting and interpretation of effect sizes in nonparametric research.

Main Methods:

  • Review of literature on effect size measures.
  • Focus on four common nonparametric tests: Mann-Whitney, Wilcoxon signed-rank, Kruskal-Wallis, and Friedman.
  • Discussion of standardized effect size calculations and interpretations.

Main Results:

  • Identified and reviewed standardized effect size measures for specified nonparametric tests.
  • Presented commonly suggested classifications for these effect sizes.
  • Provided a framework for applying effect size measures in nonparametric contexts.

Conclusions:

  • Standardized effect sizes can be effectively estimated and interpreted for nonparametric tests.
  • Consistent reporting of effect sizes improves the practical relevance of nonparametric research.
  • This review supports researchers in adopting robust effect size estimation practices.