Comparer des méthodes

	Recherche par test d'hypothèse bayésien	Recherche par test d'hypothèse
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010	Early 20th century (Fisher 1925; Neyman–Pearson 1933)
Auteur d'origine	Harold Jeffreys (formal Bayes factor framework)	Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson
Type	Quantitative research design	Quantitative confirmatory research design
Source fondatrice	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682	Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603
Alias	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design
Apparentées	5	4
Résumé	Bayesian hypothesis testing research is a quantitative design in which competing hypotheses are evaluated by updating prior beliefs with observed data to produce posterior probabilities and Bayes factors. Unlike frequentist null-hypothesis significance testing, it quantifies the relative evidence for each hypothesis, supports optional stopping, and allows accumulation of evidence across studies without inflating Type I error rates.	Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.