Sammenlign metoder

	Simuleringsbasert styrkeanalyse (Monte Carlo-styrke)	Uavhengig utvalg t-test
Fagfelt	Statistikk	Statistikk
Familie	Hypothesis test	Hypothesis test
Opprinnelsesår	2011	1908
Opphavsperson	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension	Student (W. S. Gosset)
Type	Simulation-based (Monte Carlo)	Parametric mean comparison
Opprinnelig kilde	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.
Alias	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi
Relaterte	6	4
Sammendrag	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.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.