Using randomization techniques to analyse behavioural data

Abstract

Data from behavioural studies are frequently non-normally distributed and cannot be analysed with traditional parametric statistics. Instead, behaviourists must rely on rank-transformation tests, which lose potentially valuable information present in the data. Recently, however, biologists in other disciplines have resolved similar statistical difficulties by using resampling methods. Results from Kruskal–Wallis non-parametric ANOVA and randomization tests were compared for two behavioural data sets. It was found that randomization tests were more powerful than Kruskal–Wallis, and could thus detect smaller effect sizes present in the data. In addition, the variance was calculated around the P-value at eight levels of replication ranging from 500 to 10 000, to determine the optimal number of replications for the randomization test. The variance around the P-value decreased as the number of replications increased. The P-value stabilized at 5000 replications, and thus it is recommended that at least 5000 replications be used for randomization tests on behavioural data.