Abstract
As discussed in Chapters 6, 7, and 8, statistical tests, such as the z, t, and F tests, are called parametric tests. Parametric tests are statistical tests for population parameters such as means, variances, and proportions that involve assumptions about the populations from which the samples were selected. One assumption is that these populations are normally distributed. But what if the population in a particular hypothesis-testing situation is not normally distributed? Statisticians have developed a branch of statistics known as nonparametric statistics or distribution-free statistics to use when the population from which the samples are selected is not normally distributed. In addition, nonparametric statistics can be used to test hypotheses that do not involve specific population parameters, such as μ, σ, or ρ.
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Koh, E.T., Owen, W.L. (2000). Nonparametric Statistics. In: Introduction to Nutrition and Health Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1401-5_9
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DOI: https://doi.org/10.1007/978-1-4615-1401-5_9
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