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	Analiza bayesowska tablic kontyngencji	Bayesowski test t dla prób niezależnych
Dziedzina	Statystyka	Statystyka
Rodzina	Hypothesis test	Hypothesis test
Rok powstania	1974	2009 (modern form); 1961 (Jeffreys prior framework)
Twórca	Gunel & Dickey	Harold Jeffreys (foundational); operationalized by Rouder et al.
Typ	Bayesian association test	Bayesian hypothesis test
Źródło pierwotne	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.
Inne nazwy	Bayesian chi-square test, Bayesian contingency table test, Bayes factor association test, Bayesian crosstab analysis	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Pokrewne	4	3
Podsumowanie	Bayesian cross-tabulation analysis tests whether two categorical variables are associated by computing a Bayes factor that quantifies the evidence for an association model against an independence model. Unlike classical chi-square testing, it provides a continuous measure of evidence, supports the null hypothesis directly, and updates naturally with prior knowledge about the cell probabilities.	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.