The error dispersion is a perplexity related measure of the effective number of error categories used by listeners in an identification experiment. The error difference is calculated from the error dispersions of several confusion matrices and measures the difference between these confusion matrices. The error dispersion and the error difference are largely independent of the error rate. It is shown in this paper that correlations (and the absence thereof) between the error dispersion and the error rate that are found in identification experiments can be used to infer underlying regularities in the identification process. The use of these techniques is demonstrated with examples from the literature.