Finding the number of groups in a data set, k, is an important problem in the field of unsupervised machine learning with applications across many scientific domains. The problem is difficult however, because it is ambiguous and hierarchical, and current techniques for finding k often produce unsatisfying results. Humans are adept at navigating ambiguous and hierarchical situations, and this paper measures human performance on the problem of finding k across a wide variety of data sets. We find that humans employ multiple strategies for choosing k, often simultaneously, and the number of possible interpretations of even simple data sets with very few (N < 20) samples can be quite high. In addition, two leading machine learning algorithms are compared to the human results and methods for improving these techniques are discussed.