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Winsorizing is a procedure that moderates the influence of outliers on the mean and variance and thereby creates more robust estimators of location and variability. The procedure is named for biostatistician Charles P. Winsor. Parametric inferential procedures that rely on the mean and variance (e.g., t test) become more robust when they incorporate Winsorized estimators. Winsorizing is an important tool for educational and social science researchers for two reasons. First, significance tests based on the mean and variance are very common procedures for significance testing in the social sciences. Second, surveys of the educational and psychological literature show that nonnormally distributed data are the rule rather than the exception, and even modest departures from normality disproportionately affect the mean and variance compared with other more robust estimators of location (e.g., median) and variability (e.g., median absolute deviation)
Blaine, Bruce E. (2018). "Winsorizing." The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation , 1817-1818.
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