Comparer des méthodes

	Recherche par test d'hypothèse bayésien	Recherche confirmatrice
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	1934 (Popper); widely adopted in social sciences from 1960s onward
Auteur d'origine	Harold Jeffreys (formal Bayes factor framework)	Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others
Type	Quantitative research design	Quantitative research design
Source fondatrice	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682	Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447
Alias	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	hypothesis-testing research, deductive research, theory-testing research, confirmatory study
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.	Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry.