Linganisha mbinu

	Uchambuzi wa Nguvu	Uchanganuzi wa Nguvu wa Bayesian (Uhakikisho)
Nyanja	Takwimu	Takwimu
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	1969 (1st ed.); 1988 (seminal 2nd ed.)	1986
Mwanzilishi	Jacob Cohen	Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)
Aina	Sample size and power planning	Bayesian sample size determination
Chanzo asilia	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.
Majina mbadala	sample size calculation, power calculation, sensitivity analysis, a priori power analysis	assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size)
Zinazohusiana	5	3
Muhtasari	Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.	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?