Methoden vergleichen

	Robuste Power-Analyse	Poweranalyse
Fachgebiet	Statistik	Statistik
Familie	Hypothesis test	Hypothesis test
Entstehungsjahr	1990s–2000s	1969 (1st ed.); 1988 (seminal 2nd ed.)
Urheber	Rand R. Wilcox and colleagues	Jacob Cohen
Typ	Power and sample-size planning	Sample size and power planning
Wegweisende Quelle	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
Aliasnamen	power analysis under non-normality, distribution-free power analysis, robust sample-size determination, contamination-robust power	sample size calculation, power calculation, sensitivity analysis, a priori power analysis
Verwandt	4	5
Zusammenfassung	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.	Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.