When calculating outliers using the IQR method, we find a range and define outliers outside of that range (below). Is it 'mathematically' accepted if I change the 1.5 to a 2 to get less outliers for a particular dataset? Or does this break a conventional theory?
Additionally, does the data need to follow a normal distribution to use this method?
- IQR: Q3 – Q1
- Upper bound: Q3 + (1.5 * IQR).
- Lower bound: Q3 – (1.5 *
IQR) - Outlier = outside of range [Lower, Upper]
Best Answer
There is no "outlier/not outlier" threshold. It is all a question of extremity. Some points are more extreme than others, in terms of distance from the main body of the data; such points have a greater degree of "outlierness," if you will. A z-score, or similar statistic using medians and IQRs, will suffice as a degree of "outlierness." In multiple dimensions, Mahalanobis distance and its variants serve the same purpose. There really is not much point to trying to create a "Yes/No" condition for outliers.