Compară metode

	Statistica descriptivă bayesiană	Test t pentru eșantioane independente bayesian
Domeniu	Statistică	Statistică
Familie	Hypothesis test	Hypothesis test
Anul apariției	1763/1812	2009 (modern form); 1961 (Jeffreys prior framework)
Autorul original	Thomas Bayes / Pierre-Simon Laplace	Harold Jeffreys (foundational); operationalized by Rouder et al.
Tip	Bayesian parameter estimation	Bayesian hypothesis test
Sursa seminală	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.
Denumiri alternative	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
Înrudite	5	3
Rezumat	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.