teWinsoriseren

teWinsoriseren is the statistical practice of applying Winsorization to a dataset. It involves replacing extreme data values beyond chosen cutoffs with the nearest non-extreme values, with the aim of reducing the influence of outliers on descriptive statistics and subsequent analyses. In Dutch-language writing, teWinsoriseren denotes the act of performing this data-preprocessing step.

The typical method uses symmetric cutoffs at specified percentiles. For example, mild two-sided Winsorization at 5% replaces values below the 5th percentile with the 5th percentile value and values above the 95th percentile with the 95th percentile value. Variants exist with different thresholds, such as 1% or 10%, and one-sided versions cap only one tail. The choice of thresholds depends on the data characteristics and the analyst’s tolerance for bias versus robustness.

Applications of teWinsoriseren occur in situations with outlier-prone data or small samples, including finance, psychology, and epidemiology. It can improve the stability of the mean, variance estimates, regression coefficients, and other statistics by dampening extreme observations. However, Winsorization introduces bias and alters the data distribution, particularly in the tails, which can affect tail-based inferences. The technique should be chosen with care, and results should be evaluated alongside alternative robust methods.

In context, teWinsoriseren is the Dutch-language expression of the broader concept of Winsorization, a robustness-focused data preprocessing technique used to mitigate outlier effects while preserving sample size. See also: Winsorization, robust statistics, outliers.