Dropping Incomplete Records is (not so) Straightforward.

A straightforward approach to handling missing values is dropping incomplete records from the dataset. However, for many forms of missingness, this method is known to affect the center and spread of the data distribution. In this paper, we perform an extensive empirical evaluation of the effect of the drop method on the data distribution. In particular, we analyze two scenarios that are likely to occur in practice but are not often considered in simulation studies: 1) when features are skewed rather than symmetrically distributed and 2) when multiple forms of missingness occur simultaneously in one feature. Furthermore, we investigate implications of the drop method for classification accuracy and demonstrate that dropping incomplete records is doubtful, even when test cases are dropped as well.