One commonly used technique for handling outliers in data is trimming or winsorizing. Trimming involves removing a certain percentage of extreme values from the upper and lower tails of the dataset, while winsorizing replaces extreme values with less extreme values (e.g., replacing outliers with the nearest non-outlier value). Another approach is to use robust statistical methods such as robust regression, which down weights the influence of outliers during parameter estimation. Additionally, techniques like transformation (e.g., log transformation) can be employed to reduce the impact of outliers on the analysis. Ultimately, the choice depends on the nature of the data and the specific analysis goals.
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