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| Badania weryfikacji hipotez bayesowskich× | Badania konfirmacyjne w ujęciu bayesowskim× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010 | 1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation) |
| Twórca≠ | Harold Jeffreys (formal Bayes factor framework) | Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework) |
| Typ≠ | Quantitative research design | Quantitative hypothesis-testing framework |
| Źródło pierwotne≠ | 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. DOI ↗ |
| Inne nazwy | Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing | Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR |
| Pokrewne≠ | 5 | 1 |
| Podsumowanie≠ | 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. |
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