Comparer des méthodes

	Statistiques descriptives robustes	Analyse de la taille d'effet
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1960s–1970s	1969 (first edition); 1988 (definitive second edition)
Auteur d'origine	John W. Tukey, Peter J. Huber, Frank Hampel	Jacob Cohen
Type	Resistant summary measures	Standardized magnitude estimation
Source fondatrice	Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN: 978-0201076165	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Alias	resistant statistics, outlier-resistant summary statistics, robust summary measures, robust location and scale estimation	effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis
Apparentées	5	4
Résumé	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.	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.