Methoden vergelijken

	Bayesiaans Hypotheseonderzoek	Bayesiaans modeltoetsingsonderzoek
Vakgebied	Onderzoeksontwerp	Onderzoeksontwerp
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010	1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward
Grondlegger	Harold Jeffreys (formal Bayes factor framework)	Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery
Type	Quantitative research design	Quantitative inferential research design
Oorspronkelijke bron	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682	Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.
Aliassen	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT
Verwant	5	4
Samenvatting	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.	Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it.