방법 비교

	강건 효과 크기 분석	효과 크기 분석
분야	통계학	통계학
계열	Hypothesis test	Hypothesis test
기원 연도	2005 (formalized)	1969 (first edition); 1988 (definitive second edition)
창시자	Algina, Keselman & Penfield; Wilcox	Jacob Cohen
유형	Robust effect size estimation	Standardized magnitude estimation
원전	Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10(3), 317–328.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
별칭	robust Cohen's d, trimmed-mean effect size, outlier-resistant effect size, robust standardized mean difference	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis
관련	5	4
요약	Robust effect size analysis quantifies the magnitude of a difference or association using estimators that are resistant to outliers and violations of normality. Rather than relying on classical statistics such as Cohen's d based on sample means and standard deviations, robust variants use trimmed means and Winsorized standard deviations to produce effect size estimates that accurately reflect the typical effect rather than being inflated by extreme values.	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.