Nonparametric Statistical Inference

Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material.

This classic, best-selling statistics book continues to provide in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXaot and compares these examples with corresponding hand calculations. The appendix includes an extensive collection of tables required for solving the data-oriented problems.

Jean Dickinson Gibbons is Russell Professor Emerita of Statistics at the University of Alabama.

Subhabrata Chakraborti is a Robert C. and Rosa P. Morrow Faculty Excellence Fellow and professor of statistics at the University of Alabama.