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خانوادهProcess / pipelineProcess / pipeline
سال پیدایش1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–20101935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward
پدیدآورHarold Jeffreys (formal Bayes factor framework)Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery
نوعQuantitative research designQuantitative inferential research design
منبع بنیادینJeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. DOI ↗
نام‌های دیگرBayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testingBayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT
مرتبط54
خلاصهBayesian 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.
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ScholarGateمقایسهٔ روش‌ها: Bayesian Hypothesis Testing Research · Bayesian Model Testing Research. بازیابی‌شده در 2026-06-15 از https://scholargate.app/fa/compare