ROBUSTNESS AND POWER OF THE T, PERMUTATION T AND WILCOXON TESTS
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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 th...
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.
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