Comparer des méthodes

	Statistiques descriptives bayésiennes	Analyse de puissance
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1763/1812	1969 (1st ed.); 1988 (seminal 2nd ed.)
Auteur d'origine	Thomas Bayes / Pierre-Simon Laplace	Jacob Cohen
Type	Bayesian parameter estimation	Sample size and power planning
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Alias	Bayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summaries	sample size calculation, power calculation, sensitivity analysis, a priori power analysis
Apparentées	5	5
Résumé	Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation.	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.