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Nonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. In social sciences, often, it is not possible to obtain measurements, which renders customary analysis impossible. For example, it is not possible to measure utility but is possible to rank preference, which is based on the unmeasurable utility. Nonparametric methods provide theoretically valid options for analysis, making the use of unscientific methods unnecessary. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. The only prerequisite for this book is high school level elementary algebra. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. The book is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities. It is recommended that everyone read the Introduction and Chapter 1 because determining whether data are random or normally distributed is essential in the selection of parametric versus nonparametric methods.

Nonparametric statistics. --- Nonparametric statistics --- median --- order statistics --- rank --- one sample --- two samples --- several samples --- multiple comparison --- normality --- skewness