In their recent article published in the AJBA, Wissler et al. (2022) presented an in-depth review of several methods of multiple imputation for missing ordinal or continuous data, and provided detailed and useful guidelines to handle missing data efficiently, a crucial issue in our disciplinary field. Their study showed 1) that imputation of continuous data leads to a substantially lower error than with ordinal data, and 2) that imputation errors (especially when assessed using the normalized root mean square error (NRMSE) are strongly related the amount of missing values. Their article also invited the community to evaluate these methods in other contexts. In particular, they acknowledged that the sample sizes might be considerably smaller in practical applications; and also advocated for further investigation of the performance of these methods on ordinal paleopathology data (p. 358).