Rank-permutation tests for behavior analysis, and a test for trend allowing unequal data numbers for each subject.

Abstract

We advocate for rank-permutation tests as the best choice for null-hypothesis significance testing of behavioral data, because these tests require neither distributional assumptions about the populations from which our data were drawn nor the measurement assumption that our data are measured on an interval scale. We provide an algorithm that enables exact-probability versions of such tests without recourse to either large-sample approximation or resampling approaches. We particularly consider a rank-permutation test for monotonic trend, and provide an extension of this test that allows unequal number of data points, or observations, for each subject. We provide an extended table of critical values of the test statistic for this test, and both a spreadsheet implementation and an Oracle® Java Web Start application to generate other critical values at https://sites.google.com/a/eastbayspecialists.co.nz/rank-permutation/.