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# CHAPTER 1Nonparametric Statistics: An Introduction

## 1.1    Objectives

In this chapter, you will learn the following items:

• The difference between parametric and nonparametric statistics.
• How to rank data.
• How to determine counts of observations.

## 1.2    Introduction

If you are using this book, it is possible that you have taken some type of introductory statistics class in the past. Most likely, your class began with a discussion about probability and later focused on particular methods of dealing with populations and samples. Correlations, z-scores, and t-tests were just some of the tools you might have used to describe populations and/or make inferences about a population using a simple random sample.

Many of the tests in a traditional, introductory statistics text are based on samples that follow certain assumptions called parameters. Such tests are called parametric tests. Specifically, parametric assumptions include samples that

• are randomly drawn from a normally distributed population,
• consist of independent observations, except for paired values,
• consist of values on an interval or ratio measurement scale,
• have respective populations of approximately equal variances,
• are adequately large,* and
• approximately resemble a normal distribution.

If any of your samples breaks one of these rules, you violate the assumptions of a parametric test. You do have some options, however.

You might change the nature of your study so that your data meet the needed parameters. For instance, if ...

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