Comparer des méthodes

	Bayesian Fisher's exact test	Test t bayésien pour échantillons indépendants
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1974 (Bayesian form); 1935 (Fisher's exact test)	2009 (modern form); 1961 (Jeffreys prior framework)
Auteur d'origine	Gunel & Dickey (Bayesian form); R. A. Fisher (classical exact test)	Harold Jeffreys (foundational); operationalized by Rouder et al.
Type	Bayesian hypothesis test for independence	Bayesian hypothesis test
Source fondatrice	Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 61(3), 545–557.	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.
Alias	Bayesian exact test for independence, Bayesian contingency table test, Bayes factor Fisher test, BFexact	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Apparentées	4	3
Résumé	The Bayesian Fisher's exact test evaluates independence between two categorical variables in a 2x2 table by computing a Bayes factor rather than a p-value. Using conjugate priors on cell probabilities — most commonly the Gunel-Dickey framework — it quantifies how much the observed data favor an association model over an independence model, providing a continuous scale of evidence in both directions.	The Bayesian independent samples t-test quantifies evidence for or against a mean difference between two independent groups using a Bayes factor rather than a p-value. Rooted in Jeffreys's probability framework and popularized by Rouder et al. (2009), it places a Cauchy prior on the standardized effect size and returns continuous evidence for both the null and alternative hypotheses.