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**Discuss the differences between non-parametric and parametric tests.**

Parametric and nonparametric tests are terms used by statistics shins frequently when doing analysis. Parametric and nonparametric tests referred to hypothesis test of the mean and median. The mean being the parametric and the median being a non-parametric. Parametric test assumes that your date of follows a specific distribution whereas non-parametric test also known as distribution free test do not.

**Provide an example of each and discuss when it is appropriate to use the test. Next, discuss the assumptions that must be met by the investigator to run the test.**

An example of a parametric test is a 1-2 sample T Test. It is appropriate to use this type of analysis when the sample size is greater than 20. In order to run a parametric test a researcher must know and assume that the test can perform well with skewed and non normal distributions. Another assumption a researcher must have in order to run a parametric test is the knowledge of knowing that the parametric test can perform well when the spread of each group is different.

An example of a non-parametric test is a 1 sample sign. One reason to perform a study using a non-parametric test is because you can perform a parametric test with non normal data. Another reason to use a non-parametric test in a study is when you have a very small sample size, original data, rank data or outliers which are not removable. An assumption that must be met any nonparametric test is that the data for all groups must have the same dispersion.