방법 비교

	강건 효과 크기 분석	검정력 분석
분야	통계학	통계학
계열	Hypothesis test	Hypothesis test
기원 연도	2005 (formalized)	1969 (1st ed.); 1988 (seminal 2nd ed.)
창시자	Algina, Keselman & Penfield; Wilcox	Jacob Cohen
유형	Robust effect size estimation	Sample size and power planning
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	sample size calculation, power calculation, sensitivity analysis, a priori power analysis
관련	5	5
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.	Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.