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	Bayesowski test chi-kwadrat	Bayesowski test t dla prób niezależnych
Dziedzina	Statystyka	Statystyka
Rodzina	Hypothesis test	Hypothesis test
Rok powstania	1967	2009 (modern form); 1961 (Jeffreys prior framework)
Twórca	I. J. Good; extended by Gunel, Dickey, and Wagenmakers et al.	Harold Jeffreys (foundational); operationalized by Rouder et al.
Typ	Bayesian nonparametric association test	Bayesian hypothesis test
Źródło pierwotne	Good, I. J. (1967). A Bayesian significance test for multinomial distributions. Journal of the Royal Statistical Society: Series B (Methodological), 29(3), 399–418.	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 contingency table test, Bayes factor chi-square, Bayesian goodness-of-fit test, Bayesian association test	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Pokrewne	3	3
Podsumowanie	The Bayesian chi-square test evaluates independence or goodness-of-fit in frequency tables using Bayes factors rather than classical p-values. It quantifies evidence for or against an association between categorical variables, updating prior beliefs with observed counts and delivering an odds-like ratio that distinguishes 'no evidence' from 'evidence of no effect'.	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.