পদ্ধতির তুলনা করুন

	সিমুলেশন-ভিত্তিক পাওয়ার অ্যানালাইসিস (মন্টে কার্লো পাওয়ার)	টি-পরীক্ষার জন্য পাওয়ার বিশ্লেষণ
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Hypothesis test	Hypothesis test
উদ্ভবের বছর	2011	1969
প্রবর্তক	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension	Jacob Cohen
ধরন	Simulation-based (Monte Carlo)	Sample size determination
মৌলিক উৎস	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
অপর নাম	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)	t-test power analysis, sample size calculation for t-test, Güç Analizi — t-Testi
সম্পর্কিত	6	5
সারসংক্ষেপ	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.	Power analysis for the t-test is a sample size planning procedure that determines how many participants are required to detect a mean difference of a given magnitude with acceptable probability. Formalised by Jacob Cohen in his 1969 and 1988 editions of Statistical Power Analysis for the Behavioral Sciences, it links four quantities — effect size (Cohen's d), significance level (α), statistical power (1 − β), and sample size — so that fixing any three allows calculation of the fourth.