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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.
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.