Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ величины эффекта× | t-критерий для независимых выборок× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1969 (first edition); 1988 (definitive second edition) | 1908 |
| Автор метода≠ | Jacob Cohen | Student (W. S. Gosset) |
| Тип≠ | Standardized magnitude estimation | Parametric mean comparison |
| Основополагающий источник≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Другие названия | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis | two-sample t-test, unpaired t-test, Student t-test, independent groups t-test |
| Связанные | 4 | 4 |
| Сводка≠ | Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations. | The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences. |
| ScholarGateНабор данных ↗ |
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