Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Проверка нулевой гипотезы× | Статистическая мощность и размер выборки× | |
|---|---|---|
| Область | Статистика исследований | Статистика исследований |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1925 | 1988 |
| Автор метода≠ | Ronald Fisher; Neyman & Pearson | Jacob Cohen |
| Тип | Concept | Concept |
| Основополагающий источник≠ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 |
| Другие названия≠ | NHST, hypothesis formulation, null hypothesis, alternative hypothesis | power analysis, sample size calculation, 1 minus beta, sensitivity |
| Связанные | 4 | 4 |
| Сводка≠ | Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation. | Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design: underpowered studies produce inflated effect size estimates and are unlikely to replicate. The standard benchmark is 80% power (β = 0.20), though critical studies may require 90% power. |
| ScholarGateНабор данных ↗ |
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