Methoden vergleichen
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| Simulationsbasierte Power-Analyse (Monte-Carlo-Power)× | Unabhängiger t-Test für zwei Stichproben× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Hypothesis test | Hypothesis test |
| Entstehungsjahr≠ | 2011 | 1908 |
| Urheber≠ | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension | Student (W. S. Gosset) |
| Typ≠ | Simulation-based (Monte Carlo) | Parametric mean comparison |
| Wegweisende Quelle≠ | Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94. DOI ↗ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Aliasnamen | 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 |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. |
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