방법 비교

	효과 크기 분석	ROC 분석 (수신자 조작 특성)
분야	통계학	통계학
계열	Hypothesis test	Hypothesis test
기원 연도	1969 (first edition); 1988 (definitive second edition)	1954 (signal detection); 1982 (AUC formalization)
창시자	Jacob Cohen	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
유형	Standardized magnitude estimation	Diagnostic accuracy evaluation
원전	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	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.
별칭	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
관련	4	4
요약	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.	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).