Advantages of Non-parametric Tests

Advantages of Non-parametric Tests

Non-parametric methods refer to allstatistical tests that do not work with both categorical variables and ordinal scale numbers that do not assume a normal distribution pattern prescribed by parametric tests. Examples befitting of such tests include but not limited to Mann-Whittney’s test and sign tests(Chawla & Sondhi, 2011). Their utilization affords many advantages that are of the essence to this discussion.

To begin with, non-parametric tests are useful in the handling of small samples. Despite the suitability of parametric methods in studies comprising small samples, they are not effective as the non-parametric tests. Such is the case since theyoffer accurate probabilities as compared to the parametric tests(Suresh, 2014). Clearly, this instance depicts its merit as a statistical test.

Secondly, such tests have the advantage of convenience since they require minimal computations. Central to this benefit is the fact that they do not have extraneous regulations and assumptions about data format that are characteristic of parametric tests(Chawla & Sondhi, 2011). With such a possibility, it is apparent that non-parametric tests are advantageous.

Besides, non-parametric tests are also easy to use and learn in comparison to the parametric methods. An advantage of this kind is inevitable because this type of statistical method does not have many assumptions relating to the data format that is common in parametric tests (Suresh, 2014).

Lastly, there is a possibility to work with variables that are intrinsically in ranks and numbers that only have a high potential of being represented in ranks. Variables of this kind cannot utilize the parametric tests since they cannot assume a bell-shape format common in normal distribution data sets(Chawla & Sondhi, 2011). Evidently, this illustrates its indispensability as a statistical method.

In conclusion, from the above instances, a notable deduction is the fact that non-parametric tests areessential types of statistical methods. Therefore, one should use them to achieve the various study purposes.







Chawla, D., & Sondhi, N. (2011). Research methodology: Concepts and cases.

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Suresh, S. (2014). Nursing Research and Statistics. London: Elsevier Health Sciences APAC.

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