Linganisha mbinu

	Uchanganuzi wa Nguvu wa Bayesian (Uhakikisho)	Uchanganuzi wa nguvu kulingana na uhuishaji (Nguvu ya Monte Carlo)
Nyanja	Takwimu	Takwimu
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	1986	2011
Mwanzilishi	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
Aina	Bayesian sample size determination	Simulation-based (Monte Carlo)
Chanzo asilia	O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
Majina mbadala	assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size)	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)
Zinazohusiana	3	6
Muhtasari	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?	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.