विधियों की तुलना करें

	बयेसियन परिकल्पना परीक्षण अनुसंधान	बेयसियन पुष्टिकारी अनुसंधान
क्षेत्र	अनुसंधान अभिकल्प	अनुसंधान अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010	1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation)
प्रवर्तक	Harold Jeffreys (formal Bayes factor framework)	Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework)
प्रकार	Quantitative research design	Quantitative hypothesis-testing framework
मौलिक स्रोत	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682	Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.
उपनाम	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR
संबंधित	5	1
सारांश	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 confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem.