Methoden vergelijken

	Bayesiaanse poweranalyse (Assurance)	Bayesiaanse t-toets
Vakgebied	Statistiek	Bayesiaanse statistiek
Familie	Hypothesis test	Bayesian methods
Jaar van ontstaan	1986	2009
Grondlegger	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	Rouder, Speckman, Sun, Morey & Iverson
Type	Bayesian sample size determination	Bayesian hypothesis test
Oorspronkelijke bron	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.
Aliassen	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
Verwant	3	5
Samenvatting	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.