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Defining the strenths and weakness of non-parametric tests

Under what circumstances should a nonparametric test be used? Explain. What are the strengths and weaknesses of nonparametric tests? Can the outcomes of nonparametric tests be generalized to populations?

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Question:
Nonparametric tests
Under what circumstances should a nonparametric test be used? Explain. What are the strengths and weaknesses of nonparametric tests? Can the outcomes of nonparametric tests be generalized to populations?

The assumptions of the t-tests are seriously violated. In particular, if the type of data you have is ordinal in nature and not at least interval. On such occasions an alternative approach is to use nonparametric tests. We are not going to place much emphasis on them in this unit as they are only occasionally used. But you should be aware of them and have some familiarity with them.

Nonparametric tests are also referred to as distribution-free tests. These tests have the obvious advantage of not requiring the assumption of normality or the assumption of homogeneity of variance. They compare medians rather than means and, as a result, if the data have one or two outliers, their influence is negated.

Advantages of nonparametric procedures
(1) Nonparametric test make less stringent demands of the data. For standard parametric procedures to be valid, certain underlying conditions or assumptions must be met, particularly for smaller sample sizes. The one-sample t test, for example, requires that the observations be drawn from a normally distributed population. For two independent samples, the t test has the additional requirement that the population standard deviations be equal. If these assumptions/conditions are violated, the resulting P-values and confidence intervals may not be trustworthy3. However, normality is not required for the Wilcoxon ...

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When can non-parametric tests be used and what are the strengths and weaknesses?

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