เปรียบเทียบวิธี

	การวิเคราะห์อำนาจเบย์ (Bayesian Power Analysis) (การรับประกัน)	การทดสอบทีแบบเบย์ (Bayesian t-Test)	การวิเคราะห์อำนาจการทดสอบโดยการจำลอง (Monte Carlo Power)
สาขาวิชา	สถิติศาสตร์	เบย์	สถิติศาสตร์
ตระกูล	Hypothesis test	Bayesian methods	Hypothesis test
ปีกำเนิด	1986	2009	2011
ผู้ริเริ่ม	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	Rouder, Speckman, Sun, Morey & Iverson	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
ประเภท	Bayesian sample size determination	Bayesian hypothesis test	Simulation-based (Monte Carlo)
แหล่งต้นตำรับ	O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.	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.
ชื่อเรียกอื่น	assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size)	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)
ที่เกี่ยวข้อง	3	5	6
สรุป	Bayesian power analysis — also called assurance — is a sample size determination method that replaces the frequentist notion of power with a probability-weighted average over a prior distribution on the effect size. First formalised by Spiegelhalter and Freedman (1986) and further developed by O'Hagan, Stevens and Campbell (2005), it answers the question: given our current uncertainty about the true effect, what sample size gives us a high overall probability of obtaining a statistically significant result?	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.