Comparer des méthodes

	Corrélation versus Causalité	Valeur p et signification statistique
Domaine	Statistiques de recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1965	1925
Auteur d'origine	Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)	Ronald Fisher
Type	Concept	Concept
Source fondatrice	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0-521-89560-6	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Alias	correlation and causation, causal inference, spurious correlation, confounding	p-value, significance test, statistical significance, alpha level
Apparentées	4	5
Résumé	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.	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).