Módszerek összehasonlítása

	Effect Size	P-érték és statisztikai szignifikancia
Tudományterület	Kutatási statisztika	Kutatási statisztika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1988	1925
Megalkotó	Jacob Cohen	Ronald Fisher
Típus	Concept	Concept
Alapmű	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.
Alternatív nevek	ES, Cohen's d, standardized effect, practical significance	p-value, significance test, statistical significance, alpha level
Kapcsolódó	4	5
Összefoglaló	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).