Comparer des méthodes

	Analyse de puissance	Test du Khi-deux d'indépendance
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1969 (1st ed.); 1988 (seminal 2nd ed.)	1900
Auteur d'origine	Jacob Cohen	Karl Pearson
Type	Sample size and power planning	Nonparametric test of association
Source fondatrice	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.
Alias	sample size calculation, power calculation, sensitivity analysis, a priori power analysis	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi
Apparentées	5	2
Résumé	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.	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.