STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS

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STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS

 

 

CHAPTER ONE

INTRODUCTION

1.1         Background

Nonparametric approaches are often utilized when the conditions for parametric approaches are not satisfied and in most cases when the scale of measurement is ordinal or nominal. Statistical procedure in which inferences are made about the population parameters are referred to as Parametric Statistics Cyprain (1990). Parametric approach follows certain assumptions which include samples that are randomly drawn from a normally distributed population,

  1. Consist of independent observations, except for paired values,
  2. Consist of values on an interval or ratio measurement scale,
  3. Have respective populations of approximately equal variances,
  4. Are adequately large, and
  5. Approximately resemble a normal distribution.

If any of the samples breaks one of these rules, then the assumptions of a parametric test are violated. The nature of the study might be changed to adhere to the rules. If an ordinal or nominal measurement scale is being used, the study might be redesigned to use an interval or ratio scale. Also, try to seek additional participants to enlarge the sample sizes. Unfortunately, there are times when one or neither of these changes is appropriate or even possible. There are three major parametric assumption, which are and will continue to be violated by researchers in health sciences; level of measurement, sample size and normal distribution of the dependent variable Pett (1992).

If samples do not resemble a normal distribution, you might have learned to modify them so that you can use the tests you know. There are several legitimate ways to modify your data, so you can use parametric tests. First, if the reasons can be justified, then the extreme values from samples called outlier might be removed. Second, you can apply a mathematical adjustment to each value in your samples called a transformation. That is you might square every value in a sample. Transformations do not always work, however. Third, there are more complicated methods that are so advanced. Fortunately, there is a family of statistical tests that does not demand all the parameters, or listed rules above. They are called nonparametric tests.

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STATISTICAL POWER OF HYPOTHESIS TESTING USING PARAMETRIC AND NONPARAMETRIC METHODS