Nonparametric statistics refer to methods of measurement that do not rely on assumptions that the data are drawn from a specific distribution. Nonparametric statistical methods have been widely used in various kinds of research designs to make statistical inferences. In practice, when the normality assumption on the measurements is not satisfied, parametric statistical methods might provide misleading results. In contrast, nonparametric methods make much less stringent distributional assumptions on the measurements. They are valid methods regardless of the underlying distributions of the observations. Because of this attractive advantage, ever since the first introduction of nonparametric tests in the last century, many different types of nonparametric tests have been developed to analyze various types of experimental designs. Such designs encompass one-sample design, two-sample design, randomized-block design, ...
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