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Nonparametric Statistical Methods, 3rd Edition by Eric Chicken, Douglas A. Wolfe, Myles Hollander

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Chapter 1

Introduction

1.1 Advantages of Nonparametric Methods

Roughly speaking, a nonparametric procedure is a statistical procedure that has certain desirable properties that hold under relatively mild assumptions regarding the underlying populations from which the data are obtained. The rapid and continuous development of nonparametric statistical procedures over the past c01-math-0001 decades is due to the following advantages enjoyed by nonparametric techniques:

1. Nonparametric methods require few assumptions about the underlying populations from which the data are obtained. In particular, nonparametric procedures forgo the traditional assumption that the underlying populations are normal.
2. Nonparametric procedures enable the user to obtain exact P-values for tests, exact coverage probabilities for confidence intervals, exact experimentwise error rates for multiple comparison procedures, and exact coverage probabilities for confidence bands without relying on assumptions that the underlying populations are normal.
3. Nonparametric techniques are often (although not always) easier to apply than their normal theory counterparts.
4. Nonparametric procedures are often quite easy to understand.
5. Although at first glance most nonparametric procedures seem to sacrifice too much of the basic information in the samples, theoretical efficiency investigations have shown that this is not the case. ...

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