Bandingkan metode

	Uji Faktor Bayes	Uji-t Sampel Independen	Markov Chain Monte Carlo (MCMC)
Bidang	Bayesian	Statistika	Bayesian
Keluarga	Bayesian methods	Hypothesis test	Bayesian methods
Tahun asal	1961	1908	—
Pencetus	Harold Jeffreys	Student (W. S. Gosset)	—
Tipe	Bayesian hypothesis comparison	Parametric mean comparison	Posterior sampling algorithm
Sumber perintis	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	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
Alias	bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Terkait	3	4	3
Ringkasan	The Bayes factor test, formalised by Harold Jeffreys in 1961, is a Bayesian method for comparing two competing hypotheses. Rather than returning a binary reject/retain verdict, it produces a continuous ratio BF₁₀ that quantifies how much more (or less) probable the data are under the alternative hypothesis H₁ than under the null hypothesis H₀.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.