Salīdzināt metodes

	Bejeziešu hipotēžu testēšanas pētījumi	Pētījumi par Bayes modeles testēšanu
Nozare	Pētījuma dizains	Pētījuma dizains
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010	1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward
Autors	Harold Jeffreys (formal Bayes factor framework)	Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery
Tips	Quantitative research design	Quantitative inferential research design
Pirmavots	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.
Citi nosaukumi	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT
Saistītās	5	4
Kopsavilkums	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.