Comparar métodos

	Poder Estatístico e Tamanho da Amostra	Tamanho do Efeito
Área	Estatística para pesquisa	Estatística para pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	1988	1988
Autor original	Jacob Cohen	Jacob Cohen
Tipo	Concept	Concept
Fonte seminal	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
Outros nomes	power analysis, sample size calculation, 1 minus beta, sensitivity	ES, Cohen's d, standardized effect, practical significance
Relacionados	4	4
Resumo	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.