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	Robust Effect Size Analysis	मजबूत वर्णनात्मक सांख्यिकी
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Hypothesis test	Hypothesis test
उद्भव वर्ष	2005 (formalized)	1960s–1970s
प्रवर्तक	Algina, Keselman & Penfield; Wilcox	John W. Tukey, Peter J. Huber, Frank Hampel
प्रकार	Robust effect size estimation	Resistant summary measures
मौलिक स्रोत	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
उपनाम	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
संबंधित	5	5
सारांश	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.