Sammenlign metoder

	Bayesiansk styrkeberegning (Assurance)	Bayesiansk t-test	Sekventiel Analyse (Gruppesekventielt Design)
Fagområde	Statistik	Bayesiansk	Statistik
Familie	Hypothesis test	Bayesian methods	Hypothesis test
Oprindelsesår	1986	2009	1977
Ophavsperson	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	Rouder, Speckman, Sun, Morey & Iverson	P. C. O'Brien & T. R. Fleming; P. C. Pocock
Type	Bayesian sample size determination	Bayesian hypothesis test	Sequential / adaptive hypothesis test
Oprindelig kilde	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.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.
Aliasser	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	sequential testing, group sequential design, interim analysis, Sıralı Analiz (Sequential Testing / Group Sequential Design)
Relaterede	3	5	5
Resumé	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.	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.