What is the difference between Parametric and Nonparametric?

The parametric test is a type of test that is done in statistics. This type of test is done in order to test certain assumptions regarding population. This can also provide details about the population in general like what side of the city will have more people. If the results of the parametric test coincide with what the assumptions are, this means that the assumptions are correct.

The nonparametric test is a type of test that is done with no assumptions. This is more commonly used when only testing smaller populations. This means that the data that will be acquired does not have to fit the normal distribution.

Parametric tests are a statistical test that is done based on a specific assumption made about the population. It test has information about the population parameter. Example: knowing the distribution of body weight of an entire city. The measure of central tendency used in the parametric test is the mean value.

If parameters about the population are correct, the result of the parametric analysis is often more accurate and precise than a nonparametric test. A nonparametric test is a statistical test that is not based on an assumption about the population. Unlike, parametric analysis, it doesn’t rely on parameters of the community. It is often used when the sample size is small. The measure of central tendency used in the nonparametric test is the median.