PARAMETRIC AND NONPARAMETRIC TESTS
Methodology: Research Proposal
From the onset, it would be prudent to note that there are a number of differences that exist between parametric and nonparametric tests. To begin with, one key deference between these two statistical procedure classifications relates to the making of assumptions. A number of assumptions are made when it comes to parametric tests (i.e. in relation to underlying statistical distributions) (Weaver, Morales, and Dunn, 2017). However, according to the authors, assumptions are not made in nonparametric tests. Thus, it should be noted that the latter - unlike the former - is not hinged upon any distribution. It should also be noted that unlike nonparametric tests, parametric tests have a number of conditions for validity. This is instrumental in efforts to ensure that parametric test results are reliable. According to Scott and Mazhindu (2005), the meeting of certain conditions of
This is more so the case given that as has been indicated above, they do not rely on any distribution. According to Sheskin (2010), parametric tests also happen to be less robust than nonparametric tests. This essentially means that unlike is case with parametric tests,...…and Lorch (2010), are; normality, equal variance, independence, and no outliers (187). When it comes to normality, this relates to the normal distribution of each groups data. Secondly, in relation to equal variance, the authors indicate that this relates to the assumption that there should be equal variance (in approximate terms) in each groups data. Third, independence has got to do with the assumption that each groups observations is not dependent on another groups observations. Lastly, the no outliers assumption, as Myers, Well, and Lorch (2010) point out, relates to the assumption that the group outliers are not extreme, i.e. to…
