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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de puissance pour ANOVA× | Test t pour échantillons indépendants× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Hypothesis test | Hypothesis test |
| Année d'origine≠ | 1988 | 1908 |
| Auteur d'origine≠ | Jacob Cohen | Student (W. S. Gosset) |
| Type≠ | Sample size determination | Parametric mean comparison |
| Source fondatrice≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Alias | ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi |
| Apparentées | 4 | 4 |
| Résumé≠ | Power analysis for ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs. | 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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