Which of the following statements about non-parametric tests are correct? Kruskal-Wallace test compares the differences in the ranks of data across >2 levels of one treatment to the differences in ranks within those levels. O After calculating U-values, you should use the larger of the two numbers to determine the significance of your test for a given alpha. O If your data violate the assumptions of GLM, they are not comparable to a generic normal distribution for the purposes of significance testing. The Kruskal-Wallace test can be used if data from a 2-way factorial experiment violate the assumptions of the GLM.

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Which of the following statements about non-parametric tests are correct?
O Kruskal-Wallace test compares the differences in the ranks of data across >2 levels of one
treatment to the differences in ranks within those levels.
After calculating U-values, you should use the larger of the two numbers to determine the
significance of your test for a given alpha.
If your data violate the assumptions of GLM, they are not comparable to a generic normal
distribution for the purposes of significance testing.
The Kruskal-Wallace test can be used if data from a 2-way factorial experiment violate the
assumptions of the GLM.
Spearman correlations are more powerful than Pearson correlations.
Transcribed Image Text:Which of the following statements about non-parametric tests are correct? O Kruskal-Wallace test compares the differences in the ranks of data across >2 levels of one treatment to the differences in ranks within those levels. After calculating U-values, you should use the larger of the two numbers to determine the significance of your test for a given alpha. If your data violate the assumptions of GLM, they are not comparable to a generic normal distribution for the purposes of significance testing. The Kruskal-Wallace test can be used if data from a 2-way factorial experiment violate the assumptions of the GLM. Spearman correlations are more powerful than Pearson correlations.
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