Comparer des méthodes

	Statistiques descriptives bayésiennes	Analyse de la taille d'effet
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1763/1812	1969 (first edition); 1988 (definitive second edition)
Auteur d'origine	Thomas Bayes / Pierre-Simon Laplace	Jacob Cohen
Type	Bayesian parameter estimation	Standardized magnitude estimation
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	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis
Apparentées	5	4
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.	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.