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	Bayesowska jedno-próbowa statystyka t	Bayesowski test t dla prób niezależnych
Dziedzina	Statystyka	Statystyka
Rodzina	Hypothesis test	Hypothesis test
Rok powstania	2009	2009 (modern form); 1961 (Jeffreys prior framework)
Twórca	Rouder, Speckman, Sun, Morey & Iverson	Harold Jeffreys (foundational); operationalized by Rouder et al.
Typ	Bayesian mean-vs-constant comparison	Bayesian hypothesis test
Źródło pierwotne	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.	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 single-sample t-test, Bayes factor one-sample t-test, JZS one-sample Bayes factor, Bayesian location test	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Pokrewne	2	3
Podsumowanie	The Bayesian one-sample t-test compares a single group's mean against a fixed reference value using a Bayes factor rather than a p-value. It quantifies the evidence the data provide for the null hypothesis (mean equals the reference) versus the alternative, and yields a full posterior distribution over the effect size — enabling statements about practical magnitude, not just a binary reject-or-retain decision.	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.