Сравнение методов

	Робастные описательные статистики	Анализ величины эффекта
Область	Статистика	Статистика
Семейство	Hypothesis test	Hypothesis test
Год появления	1960s–1970s	1969 (first edition); 1988 (definitive second edition)
Автор метода	John W. Tukey, Peter J. Huber, Frank Hampel	Jacob Cohen
Тип	Resistant summary measures	Standardized magnitude estimation
Основополагающий источник	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
Другие названия	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
Связанные	5	4
Сводка	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.