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Parametric and Nonparametric Data Identification
It is not always easy deciding whether data should be treated as parametric or nonparametric. A parametric test is a test that requires a parametric assumption, such as normality. A nonparametric test does not rely on parametric assumptions like normality (Simon, 2005). Whichever test a researcher decides to use, one must have a basic understanding of both parametric and nonparametric data.
Parametric data is data that can be measured. For example, heights, weight, depth, amount of money, square footage are all forms of parametric data. These are all data with parameters. Interval and ratio measurements are considered parametric.
Data is considered parametric if it has the following three assumptions: normality, equal variances, and independence. First, is the data obtained from a population that is considered normal? Second, the populations from which the data is obtained should have equal variances. The F-test can be......

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