Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Хи-квадрат тест независимости× | Анализ мощности для дисперсионного анализа (ANOVA)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1900 | 1988 |
| Автор метода≠ | Karl Pearson | Jacob Cohen |
| Тип≠ | Nonparametric test of association | Sample size determination |
| Основополагающий источник≠ | Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| Другие названия | chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi | ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA |
| Связанные≠ | 2 | 4 |
| Сводка≠ | The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900. | 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. |
| ScholarGateНабор данных ↗ |
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