Σύγκριση μεθόδων

	Δοκιμή t Bayes	Ανάλυση κατά στάδια (Σχεδιασμός κατά διαδοχικές ομάδες)	Ανάλυση Ισχύος Βάσει Προσομοίωσης (Ισχύς Monte Carlo)
Πεδίο	Μπεϋζιανή Στατιστική	Στατιστική	Στατιστική
Οικογένεια	Bayesian methods	Hypothesis test	Hypothesis test
Έτος προέλευσης	2009	1977	2011
Δημιουργός	Rouder, Speckman, Sun, Morey & Iverson	P. C. O'Brien & T. R. Fleming; P. C. Pocock	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
Τύπος	Bayesian hypothesis test	Sequential / adaptive hypothesis test	Simulation-based (Monte Carlo)
Θεμελιώδης πηγή	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.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
Εναλλακτικές ονομασίες	bayesian two-sample t-test, bayes factor t-test, Bayesçi t-Testi	sequential testing, group sequential design, interim analysis, Sıralı Analiz (Sequential Testing / Group Sequential Design)	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)
Συναφείς	5	5	6
Σύνοψη	The Bayesian t-test, formalised by Rouder and colleagues in 2009, is a two-group comparison method that works within a Bayesian framework. Instead of a p-value, it produces a Bayes Factor (BF₁₀) that quantifies the evidence the data provide for the alternative hypothesis relative to the null, and it reports the full posterior distribution of the standardised effect size δ with a highest-density interval.	Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), and remains the standard for confirmatory clinical trials and rigorous A/B experiments.	Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016.