Comparer des méthodes

	Analyse de puissance robuste	Analyse de la taille d'effet
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1990s–2000s	1969 (first edition); 1988 (definitive second edition)
Auteur d'origine	Rand R. Wilcox and colleagues	Jacob Cohen
Type	Power and sample-size planning	Standardized magnitude estimation
Source fondatrice	Luh, W.-M., & Guo, J.-H. (2010). Approximate sample size formulas for the two-sample trimmed mean test with unequal variances. British Journal of Mathematical and Statistical Psychology, 63(1), 83–100.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Alias	power analysis under non-normality, distribution-free power analysis, robust sample-size determination, contamination-robust power	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis
Apparentées	4	4
Résumé	Robust power analysis computes the statistical power or required sample size for hypothesis tests that use robust estimators — such as trimmed means or Winsorized variances — instead of ordinary means and standard deviations. It protects against inflated or deflated power estimates that arise when data contain outliers, heavy tails, or skewness that violate classical normality assumptions.	Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations.