# Differences between Parametric and Non-parametric Tests

Differences between Parametric and Non-parametric Tests

Inferential statistics is an essential element in the medical world since it helps one to make a conclusion about study variables by testing the hypothesis. Central to this branch of statistics are the two types of tests that help this function. They include parametric and non-parametric tests (Chawla & Sondhi, 2011). The two have several differences, which are of the essence to this discussion.

To begin with, the variables utilized in parametric tests differ from the ones that inform the existence of non-parametric tests. Such is the case with the parametric methods are only useful when dealing with numbers that can form a bell curve in a normal distribution. For instance, all numbers that follow a certain order can correctly demonstrate a normal distribution utilized by such methods. On the other hand, non-parametric arrangements utilize variables that cannot fit the normal distribution requirement that parametric tests require. Categorical variables and ordinal scales are befitting examples that use the non-parametric methods (Macnee & McCabe, 2008).

Additionally, the two tests are distinct regarding the specific methods to establish the variation in two groups of a particular study. For example, the parametric use predominantly the t and z test to ascertain the relationship between two groups. On the contrary, the non-parametric rely on tests such as the sign test, Mann-Whitney, and ANOVA to achieve this purpose (Chawla & Sondhi, 2011). Apparently, these specific tests point to their difference as statistical methods.

Lastly, the size of the sample under assessment also helps distinguish between the two methods. In parametric tests, both the small and large samples are easy to handle. However, only small samples are characteristic of non-parametric methods (Macnee & McCabe, 2008). Thus, this is a further proof of the variation between the two tests.

In conclusion, parametric and non-parametric tests are two completely different tests that one must consider understanding their indications to have good outcomes. Failure to do so, however, only spells out the wrong use of such tests.

References

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

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Macnee, C. L., & McCabe, S. (2008). Understanding nursing research: Using research in evidence-based practice. Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins.

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