Persistent homology, a recent development in computational topology, has shown to be useful for analyzing high dimensional non-linear data. In this talk, we connect computational topology with the traditional analysis of variance and demonstrate this synergy on a three-dimensional orthodontic landmark data set derived from the maxillary complex. (Joint work with Jennifer Gamble and Peter Kim)