title: Why the null matters: statistical tests, random walks and evolution.
abstract: A number of statistical tests have been developed to determine what type of dynamics underlie observed changes in morphology in evolutionary time series, based on the pattern of change within the time series. The theory of the 'scaled maximum', the 'log-rate-interval' (LRI) method, and the Hurst exponent all operate on the same principle of comparing the maximum change, or rate of change, in the observed dataset to the maximum change expected of a random walk. Less change in a dataset than expected of a random walk has been interpreted as indicating stabilizing selection, while more change implies directional selection. The 'runs test' in contrast, operates on the sequencing of steps, rather than on excursion. Applications of these tests to computer generated, simulated time series of known dynamical form and various levels of additive noise indicate that there is a fundamental asymmetry in the rate of type II errors of the tests based on excursion: they are all highly sensitive to noise in models of directional selection that result in a linear trend within a time series, but are largely noise immune in the case of a simple model of stabilizing selection. Additionally, the LRI method has a lower sensitivity than originally claimed, due to the large range of LRI rates produced by random walks. Examination of the published results of these tests show that they have seldom produced a conclusion that an observed evolutionary time series was due to directional selection, a result which needs closer examination in light of the asymmetric response of these tests.
authors.affiliation: Department of Physics, Canisius College, Buffalo, NY 14208, USA. sheets@gort.canisius.edu
authors.forename: H D
authors.initials: HD
authors.lastname: Sheets
authors.forename: C E
authors.initials: CE
authors.lastname: Mitchell
mesh_terms: D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D008957:Models, Genetic; D015233:Models, Statistical