DISTRIBUTION-FREE AND ROBUST STATISTICAL-METHODS - VIABLE ALTERNATIVES TO PARAMETRIC STATISTICS

After making a case for the prevalence of nonnormality, this paper att empts to introduce some distribution-free and robust techniques to eco logists and to offer a critical appraisal of the potential advantages and drawbacks of these methods. The techniques presented fall into two distinct categories, methods based on ranks and ''computer-intensive' ' techniques. Distribution-free rank tests have features that can be r ecommended. They free the practitioner from concern about the underlyi ng distribution and are very robust to outliers. If the distribution u nderlying the observations is other than normal, rank tests tend to be more efficient than their parametric counterparts. The absence, in co mputing packages, of rank procedures for complex designs may, however, severely limit their use for ecological data. An entire body of novel distribution-free methods has been developed in parallel with the inc reasing capacities of today's computers to process large quantities of data. These techniques either reshuffle or resample a data set (i.e., sample with replacement) in order to perform their analyses. The form er we shall refer to as ''permutation'' or ''randomization'' methods a nd the latter as ''bootstrap'' techniques. These computer-intensive me thods provide new alternatives for the problem of a small and/or unbal anced data set, and they may be the solution for parameter estimation when the sampling distribution cannot be derived analytically. Caution must be exercised in the interpretation of these estimates because co nfidence limits may be too small.