Methoden vergelijken

	Bayesiaanse One-Way ANOVA	Bayesiaanse t-test voor onafhankelijke steekproeven
Vakgebied	Statistiek	Statistiek
Familie	Hypothesis test	Hypothesis test
Jaar van ontstaan	1961 (foundations); 2012 (ANOVA Bayes factors)	2009 (modern form); 1961 (Jeffreys prior framework)
Grondlegger	Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA)	Harold Jeffreys (foundational); operationalized by Rouder et al.
Type	Bayesian hypothesis test	Bayesian hypothesis test
Oorspronkelijke bron	Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.	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.
Aliassen	Bayesian ANOVA, BF ANOVA, Bayes factor one-way ANOVA, Bayesian F-test	Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test
Verwant	3	3
Samenvatting	Bayesian one-way ANOVA tests whether the means of three or more independent groups differ by computing a Bayes factor — a ratio that quantifies how much more likely the data are under a model that allows group differences than under the null model that assumes equal means. Unlike the classical F-test, it provides direct evidence for or against the null hypothesis rather than merely rejecting or retaining it.	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.