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Outliers increase the standard deviation. Mean is most affected by outliers, since all values in a sample are given the same weight when calculating mean. A value that is far removed from the mean is going to likely skew your results and increase the standard deviation. Say you have five values: 2, 1, 2, 1.5, and 2.1. Your mean would be 1.72 and your standard deviation would be 0.47.
Outliers in data can distort predictions and affect the accuracy, if you donât detect and handle them appropriately especially in regression models. Treating or altering the outlier/extreme values in genuine observations is not a standard operating procedure. However, it is essential to understand their impact on your predictive models. It is left to the best judgement of the investigator to decide whether treating outliers is necessary and how to go about it.