مقایسهٔ روش‌ها

	آزمون تی نمونه‌های مستقل	زنجیره مارکوف مونت کارلو (MCMC)
حوزه	آمار	بیزی
خانواده	Hypothesis test	Bayesian methods
سال پیدایش	1908	—
پدیدآور	Student (W. S. Gosset)	—
نوع	Parametric mean comparison	Posterior sampling algorithm
منبع بنیادین	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
نام‌های دیگر	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)
مرتبط	4	3
خلاصه	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.