Porovnat metody

	Bayesovská deskriptivní statistika	Bayesův t-test pro nezávislé výběry
Obor	Statistika	Statistika
Rodina	Hypothesis test	Hypothesis test
Rok vzniku	1763/1812	2009 (modern form); 1961 (Jeffreys prior framework)
Tvůrce	Thomas Bayes / Pierre-Simon Laplace	Harold Jeffreys (foundational); operationalized by Rouder et al.
Typ	Bayesian parameter estimation	Bayesian hypothesis test
Původní zdroj	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	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.
Další názvy	Bayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summaries	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Příbuzné	5	3
Shrnutí	Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation.	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.