Data analysis is conducted via parametric or nonparametric methods, depending on the data. Authors state that parametric techniques are more robust with regard to Type I error and more powerful than nonparametric techniques. Nonparametric methods are good alternatives to parametric methods being robust and powerful under non-normality. Permutation tests offer advantages compared to parametric tests as they require fewer assumptions. It was found that they are robust with regard to Type I error and powerful. However, permutation tests maintain the Type I error to the nominal ? with no evidence that they are more powerful than nonparametric tests. Monte Carlo simulations were used to investigate the Type I error and power of the t-, permutation t- and the Wilcoxon tests for some distributions.It was found that, under normality, the t and permutation t-tests were robust with regard to Type I error compared to the Wilcoxon test. They were also slightly more powerful than the Wilcoxon test. However under non-normality, the Wilcoxon test was robust with regard to Type I error and much more powerful than the t and permutation t-tests.

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