Normally to handle outliers we use Turkeyâ€™s method to detect extreme values. Multivariate methods also exist to detect and remove outliers.

Minkowski error, however, allows us to keep our outliers and simply reduce the impact they have on our data. Instead of measuring our model using the squared error, we raise error to a power less than two: say 1.5. In this way, the contribution that an outlier gives is lessened and we can keep the data. To put this in concrete terms, an error of 10 raised to the 1.5 will contribute a sum of only 31.62 as opposed to 100 (by sum of squares). See.