Comparar métodos

	Correlação vs. Causalidade	Tamanho do Efeito
Área	Estatística para pesquisa	Estatística para pesquisa
Família	Process / pipeline	Process / pipeline
Ano de origem	1965	1988
Autor original	Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)	Jacob Cohen
Tipo	Concept	Concept
Fonte seminal	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0-521-89560-6	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Outros nomes	correlation and causation, causal inference, spurious correlation, confounding	ES, Cohen's d, standardized effect, practical significance
Relacionados	4	4
Resumo	Correlation measures the strength and direction of association between two variables; causation implies that changes in one variable directly produce changes in another. A strong correlation (e.g., r = 0.9) does not prove causation. Classic examples abound: shoe size and reading ability are correlated in children (confounded by age), but shoe size does not cause reading ability. Understanding when correlation implies causation requires evaluating study design, confounding variables, temporal precedence, and mechanism. Randomized experiments offer the strongest causal evidence; observational studies must carefully control for confounders.	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.