Nonparametric Hypothesis Testing

A novel presentation of rank and permutation tests, with accessible guidance to applications in Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.

Key Features:

• Examines the most widely used methodologies of nonparametric testing.
• Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies.
• Presents and discusses solutions to the most important and frequently encountered real problems in different fields.

Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.

Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.

Table of Contents

Chapter 1 One- and two-sample location problems, tests for symmetry and tests on a single distribution
Chapter 2 Comparing variability and distributions
Chapter 3 Comparing more than two samples
Chapter 4 Paired samples and repeated measures
Chapter 5 Tests for categorical data
Chapter 6 Testing for correlation and concordance
Chapter 7 Tests for heterogeneity

Appendix A Selected critical values for the null distribution of the peak-known Mack–Wolfe statistic
Appendix B Selected critical values for the null distribution of the peak-unknown Mack–Wolfe statistic
Appendix C Selected upper-tail probabilities for the null distribution of the Page L statistic
Appendix D R functions and codes