A Monte-Carlo study of asymptotically robust tests for correlation coefficients

Monte-Carlo simulation is used to compare the small-sample performance of the usual normal theory procedures for inference about correlation coefficients with that of two asymptotically robust procedures, one of which is based on a grouping of the observations and the other on the jackknife technique. The sampled distributions comprise the normal and five nonnormal distributions. The small-sample results support the conclusion based on asymptotic theory that the normal test is not robust. The jackknife procedure works well for most of the sampled distributions.