The statistical power of a given test involves the probability of rejecting the negative researcherâ€™s speculation about the problem that has to be solved especially when it is not true and that it should be rejected. The power of parametric is usually calculated from graphs, tables and formulas based on their underlying distribution. One of the most common ways of calculating the power of a non-parametric test is through the use of the Monte carol simulation methods (Field, 2009).
The statistical power is believed to be lower in the non â€“ parametric tests as compared to the parametric type of tests. This is because a non-parametric one cannot test the same set of variables as the corresponding parametric test. As far as other things might be equal, non-parametric techniques tend to be less powerful tests insignificance in comparison to their parametric counterparts.
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