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Nonparametric Statistics: A Step-by-Step Approach, 2nd Edition by Dale I. Foreman, Gregory W. Corder

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CHAPTER 2Testing Data for Normality

2.1    Objectives

In this chapter, you will learn the following items:

  • How to find a data sample's kurtosis and skewness and determine if the sample meets acceptable levels of normality.
  • How to use SPSS® to find a data sample's kurtosis and skewness and determine if the sample meets acceptable levels of normality.
  • How to perform a Kolmogorov–Smirnov one-sample test to determine if a data sample meets acceptable levels of normality.
  • How to use SPSS to perform a Kolmogorov–Smirnov one-sample test to determine if a data sample meets acceptable levels of normality.

2.2    Introduction

Parametric statistical tests, such as the t-test and one-way analysis of variance, are based on particular assumptions or parameters. The data samples meeting those parameters are randomly drawn from a normal population, based on independent observations, measured with an interval or ratio scale, possess an adequate sample size (see Chapter 1), and approximately resemble a normal distribution. Moreover, comparisons of samples or variables should have approximately equal variances. If data samples violate one or more of these assumptions, you should consider using a nonparametric test.

Examining the data gathering method, scale type, and size of a sample are fairly straightforward. However, examining a data sample's resemblance to a normal distribution, or its normality, requires a more involved analysis. Visually inspecting a graphical representation of a sample, such ...

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