In recent years, the field of nonparametrics has continued to grow at a rapid rate. This may be partially due to the complementary increase in computing power, but is perhaps more reflective of the need for more flexible and complex models to describe the everincreasing amount and complexity of data that face us. This unprecedented growth has signaled the need for a special issue on nonparametric statistics, and the Guest Editors were charged with compiling such an issue, one that would serve as an entrance to this vast body of knowledge. As we know, contemporary nonparametric statistics embraces far more than traditional distributionfree rank tests and their corresponding R-estimation methods developed for simple analysis of variance designs. Indeed, nonparametric statistics can and should be broadly defined to include all methodology that does not use a model based on a single parametric family. Now included under the rubric of nonparametric methods are such diverse fields as general linear models (including multivariate data structures, nonparametric survival analysis, nonparametric curve estimation and bootstrap methods), as well as others illustrated in the articles in this issue. The traditional methods are well documented in many good texts and include the Wilcoxon signed rank test and Hodges–Lehmann estimate, the Wilcoxon–Mann–Whitney rank sum test and the Kruskal–Wallis test. Some of these methods date back to the 1940s and 1950s, and much of their popularity is due to the work of Erich Lehmann and others