During week 1 (ch.8) of the text, we read about the “power” of a test. Remember that the power of a test can be summarized as the probability of rejecting a false null hypothesis. In other words, the power of a test is how likely we are to “get it right.” This provides the foundation for a potentially interesting discussion. By now, each of you has identified the assumption differences between parametric and nonparametric tests, but we have yet to discuss the differences in terms of power. Let’s use this thread to do so. Let’s assume that you wish to test a hypothesis and are able to use a t-test (parametric test) for the analysis. Could you also use a nonparametric test for this? Why or why not? Assuming an identical level of significance for each test, which would be more powerful and why?

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