Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Effect Size× | P-Value и Статистическая Значимость× | |
|---|---|---|
| Область | Статистика исследований | Статистика исследований |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1988 | 1925 |
| Автор метода≠ | Jacob Cohen | Ronald Fisher |
| Тип | Concept | Concept |
| Основополагающий источник≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| Другие названия | ES, Cohen's d, standardized effect, practical significance | p-value, significance test, statistical significance, alpha level |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings. | The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05). |
| ScholarGateНабор данных ↗ |
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