Abstract
As you learned in Chapter 15, when we use chi-square tests for frequency data and we are not estimating population parameters, we are conducting nonparametric tests. A whole class of nonparametric procedures is available for data that are ordinal (ranks) in nature. Some data are ordinal by their very definition, such as employee rankings, while in other cases we convert interval or ratio data to ranks because the data violate distributional assumptions such as linearity, normality, or equality of variance. As a rule of thumb, nonparametric tests are generally less powerful than parametric tests, but that is not always the case.
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© 2012 Larry Pace
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Pace, L. (2012). Nonparametric Tests. In: Beginning R. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-4555-1_16
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DOI: https://doi.org/10.1007/978-1-4302-4555-1_16
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4302-4554-4
Online ISBN: 978-1-4302-4555-1
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