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Bayesiläinen kuvaileva tilastotiede×Bayesiläinen riippumattomien otosten t-testi×
TieteenalaTilastotiedeTilastotiede
MenetelmäperheHypothesis testHypothesis test
Syntyvuosi1763/18122009 (modern form); 1961 (Jeffreys prior framework)
KehittäjäThomas Bayes / Pierre-Simon LaplaceHarold Jeffreys (foundational); operationalized by Rouder et al.
TyyppiBayesian parameter estimationBayesian hypothesis test
AlkuperäislähdeGelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955Rouder, 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. DOI ↗
RinnakkaisnimetBayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summariesBayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Liittyvät53
TiivistelmäBayesian 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.
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ScholarGateVertaile menetelmiä: Bayesian descriptive statistics · Bayesian Independent Samples t-test. Haettu 2026-06-17 osoitteesta https://scholargate.app/fi/compare