Journal of Data Science

Abstract: The assessment of modality or “bumps” in distributions is of in terest to scientists in many areas. We compare the performance of four statistical methods to test for departures from unimodality in simulations, and further illustrate the four methods using well-known ecological datasets on body mass published by Holling in 1992 to illustrate their advantages and disadvantages. Silverman’s kernel density method was found to be very conservative. The excess mass test and a Bayesian mixture model approach showed agreement among the data sets, whereas Hall and York’s test pro vided strong evidence for the existence of two or more modes in all data sets. The Bayesian mixture model also provided a way to quantify the un certainty associated with the number of modes. This work demonstrates the inherent richness of animal body mass distributions but also the difficulties for characterizing it, and ultimately understanding the processes underlying them.