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	Bayesian t-Test	Analisi della potenza basata su simulazione (Potenza di Monte Carlo)
Campo	Bayesiano	Statistica
Famiglia	Bayesian methods	Hypothesis test
Anno di origine	2009	2011
Ideatore	Rouder, Speckman, Sun, Morey & Iverson	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
Tipo	Bayesian hypothesis test	Simulation-based (Monte Carlo)
Fonte seminale	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.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
Alias	bayesian two-sample t-test, bayes factor t-test, Bayesçi t-Testi	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)
Correlati	5	6
Sintesi	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.	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.