方法对比

	贝叶斯假设检验研究	贝叶斯确认性研究
领域	研究设计	研究设计
方法族	Process / pipeline	Process / pipeline
起源年份	1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010	1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation)
提出者	Harold Jeffreys (formal Bayes factor framework)	Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework)
类型	Quantitative research design	Quantitative hypothesis-testing framework
开创性文献	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.
别名	Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing	Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR
相关	5	1
摘要	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.