Сравнение на методи

	Статистическа мощност и размер на извадката	Размер на ефекта (Effect Size)
Област	Статистика за изследвания	Статистика за изследвания
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1988	1988
Създател	Jacob Cohen	Jacob Cohen
Тип	Concept	Concept
Основополагащ източник	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
Други названия	power analysis, sample size calculation, 1 minus beta, sensitivity	ES, Cohen's d, standardized effect, practical significance
Свързани	4	4
Резюме	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.