Сравнение методов

	Анализ робастных размеров эффекта	Робастные описательные статистики
Область	Статистика	Статистика
Семейство	Hypothesis test	Hypothesis test
Год появления	2005 (formalized)	1960s–1970s
Автор метода	Algina, Keselman & Penfield; Wilcox	John W. Tukey, Peter J. Huber, Frank Hampel
Тип	Robust effect size estimation	Resistant summary measures
Основополагающий источник	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.	Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN: 978-0201076165
Другие названия	robust Cohen's d, trimmed-mean effect size, outlier-resistant effect size, robust standardized mean difference	resistant statistics, outlier-resistant summary statistics, robust summary measures, robust location and scale estimation
Связанные	5	5
Сводка	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.	Robust descriptive statistics summarize the location, spread, and shape of a dataset using measures that remain meaningful even when a fraction of the data contains outliers or severe departures from normality. Core tools include the median, trimmed mean, interquartile range (IQR), and median absolute deviation (MAD), all of which are resistant to contamination that would distort the classic mean and standard deviation.