In general, the Kruskal-Wallis test is as powerful as parametric ANOVA.
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The Kruskal-Wallis test is a nonparametric alternate for parametric one-way ANOVA, independent groups design.
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The value of fo can be found by multiplying the marginals for that cell, and dividing by N.
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As a general rule an investigator should use parametric tests whenever possible to help minimize the probability of making a Type II error.
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In general, nonparametric tests have fewer requirements or assumptions about population characteristics than parametric tests do.
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Anytime it is appropriate to use a nonparametric statistic it is appropriate to use a parametric statistic.
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Nonparametric tests are generally more powerful than parametric tests.
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The Kruskal-Wallis test requires there are at least 5 scores in each sample.
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When using the Kruskal-Wallis test, tied scores between conditions are thrown out.
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The Kruskal-Wallis test requires only ordinal scaling of the dependent variable.
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