পদ্ধতির তুলনা করুন

	বেয়েশীয় পাওয়ার বিশ্লেষণ (নিশ্চয়তা)	গ্রুপ সিকুয়েন্সিয়াল ডিজাইন সহ অনুক্রমিক বিশ্লেষণ	সিমুলেশন-ভিত্তিক পাওয়ার অ্যানালাইসিস (মন্টে কার্লো পাওয়ার)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Hypothesis test	Hypothesis test	Hypothesis test
উদ্ভবের বছর	1986	1977	2011
প্রবর্তক	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	P. C. O'Brien & T. R. Fleming; P. C. Pocock	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
ধরন	Bayesian sample size determination	Sequential / adaptive hypothesis test	Simulation-based (Monte Carlo)
মৌলিক উৎস	O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.	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.
অপর নাম	assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size)	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)
সম্পর্কিত	3	5	6
সারসংক্ষেপ	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?	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.