Comparar métodos

	Valor p y significancia estadística	Tamaño del efecto
Campo	Estadística para la investigación	Estadística para la investigación
Familia	Process / pipeline	Process / pipeline
Año de origen	1925	1988
Autor original	Ronald Fisher	Jacob Cohen
Tipo	Concept	Concept
Fuente seminal	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
Alias	p-value, significance test, statistical significance, alpha level	ES, Cohen's d, standardized effect, practical significance
Relacionados	5	4
Resumen	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.