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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test t bayésien× | Test du facteur de Bayes× | |
|---|---|---|
| Domaine | Bayésien | Bayésien |
| Famille | Bayesian methods | Bayesian methods |
| Année d'origine≠ | 2009 | 1961 |
| Auteur d'origine≠ | Rouder, Speckman, Sun, Morey & Iverson | Harold Jeffreys |
| Type≠ | Bayesian hypothesis test | Bayesian hypothesis comparison |
| Source fondatrice≠ | 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 ↗ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682 |
| Alias≠ | bayesian two-sample t-test, bayes factor t-test, Bayesçi t-Testi | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | The Bayesian t-test, formalised by Rouder and colleagues in 2009, is a two-group comparison method that works within a Bayesian framework. Instead of a p-value, it produces a Bayes Factor (BF₁₀) that quantifies the evidence the data provide for the alternative hypothesis relative to the null, and it reports the full posterior distribution of the standardised effect size δ with a highest-density interval. | The Bayes factor test, formalised by Harold Jeffreys in 1961, is a Bayesian method for comparing two competing hypotheses. Rather than returning a binary reject/retain verdict, it produces a continuous ratio BF₁₀ that quantifies how much more (or less) probable the data are under the alternative hypothesis H₁ than under the null hypothesis H₀. |
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