Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ мощности× | Хи-квадрат тест независимости× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1969 (1st ed.); 1988 (seminal 2nd ed.) | 1900 |
| Автор метода≠ | Jacob Cohen | Karl Pearson |
| Тип≠ | Sample size and power planning | Nonparametric test of association |
| Основополагающий источник≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | 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 ↗ |
| Другие названия | sample size calculation, power calculation, sensitivity analysis, a priori power analysis | chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi |
| Связанные≠ | 5 | 2 |
| Сводка≠ | Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study. | 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. |
| ScholarGateНабор данных ↗ |
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