ABSTRACT
The statistical inference techniques presented in Chapter 8 and Chapter 9 are based on complete satisfaction of all of the assumptions made in the derivations of their sampling distributions. Indeed, many of the techniques in Chapters 8 and 9 are commonly referred to as parametric techniques since not only was the form of the underlying population (generally normal) stated, but so was one or more of the underlying distribution’s parameters. This chapter introduces both distribution-free tests as well as nonparametric tests. The collection of inferential techniques known as distribution-free is based on functions of the sample observations whose corresponding random variable has a distribution that is independent of the specific distribution function from which the sample was drawn. Consequently, assumptions with respect to the underlying population are not required. Nonparametric tests involve tests of a hypothesis where there is no statement about the distribution’s parameters; however, it is common practice to refer collectively to both distribution-free tests and nonparametric tests simply as nonparametric methods.
