Another advantage of the non-parametric tests over the parametric ones is that they are universal. That is, they can be used even in situations where it would be appropriate to use the other type of test for example, one can convert a score into a rank while he or she cannot convert the rank into a score (Bennett, 2006).

Also, non-parametric procedures are used by people involved in certain research whereby these people do not know anything to do with the parameters of the variables of interest in the population being researched (Bennett, 2006).

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Another advantage of the non-parametric method is that it tends to describe the distribution of the variable of interest, since it is independent of the estimation of the parameters, such as the mean and the standard deviation (Bennett, 2006).

While one uses the non-parametric type of tests, he or she can have a more relaxed approach to the statistical data to analyze.

Also, these parametric tests are usually based on the ordering of the data. In other words, they are usually ranked systematically, as opposed to the other type of test where the sets of data are only distributed and not numbered in any order. In doing so, it will therefore be very easy to calculate the probability of a certain given set of data