Compară metode

	Puterea statistică și dimensiunea eșantionului	Effect Size
Domeniu	Statistică pentru cercetare	Statistică pentru cercetare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1988	1988
Autorul original	Jacob Cohen	Jacob Cohen
Tip	Concept	Concept
Sursa seminală	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Denumiri alternative	power analysis, sample size calculation, 1 minus beta, sensitivity	ES, Cohen's d, standardized effect, practical significance
Înrudite	4	4
Rezumat	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.	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.