Сравнение методов

	P-Value и Статистическая Значимость	Effect Size
Область	Статистика исследований	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1925	1988
Автор метода	Ronald Fisher	Jacob Cohen
Тип	Concept	Concept
Основополагающий источник	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Другие названия	p-value, significance test, statistical significance, alpha level	ES, Cohen's d, standardized effect, practical significance
Связанные	5	4
Сводка	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).	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.