Nonparametric Data Analysis provides a comprehensive presentation of nonparametric methods for statistical modeling and evaluation of experiments, describing many long-established procedures from a novel perspective. This approach allows for a consistent treatment of metric data with both continuous and arbitrarily discontinuous distributions, as well as the analysis of ordinal and even dichotomous data using the same methods. The modeling technique presented enables an elegant generalization of the well-known rank methods to a variety of experimental designs. Notably, it features an excellent representation of the nonparametric Behrens-Fisher problem and a clear introduction to the analysis of multifactorial experimental designs using matrix techniques. Numerous examples are provided for performing calculations.