Comparer des méthodes

	Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)	Analyse de la taille d'effet
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1954 (signal detection); 1982 (AUC formalization)	1969 (first edition); 1988 (definitive second edition)
Auteur d'origine	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)	Jacob Cohen
Type	Diagnostic accuracy evaluation	Standardized magnitude estimation
Source fondatrice	Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Alias	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis
Apparentées	4	4
Résumé	ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination).	Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations.