Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Poder Baseada em Simulação (Poder de Monte Carlo)× | Análise de Variância Unifatorial× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Hypothesis test | Hypothesis test |
| Ano de origem≠ | 2011 | 1925 |
| Autor original≠ | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension | Ronald A. Fisher |
| Tipo≠ | Simulation-based (Monte Carlo) | Parametric mean comparison |
| Fonte seminal≠ | Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94. DOI ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ |
| Outros nomes | Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power) | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | 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. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. |
| ScholarGateConjunto de dados ↗ |
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