The following assumptions must be met in order to run parametric test: the sample has to be taken from a population in which the variance can be calculated, the level of measurement should have an normal distribution using either ordinal or interval level data, the collected data is able to be treated as random samples (Gray, Grove, & Sutherland, 2017). A recent study compared if nonparametric versus parametric test were better in the biomedical research. The study found that if a nonparametric test was used to determined how many patients/cases to include a larger sample size would be required in comparison to a parametric test (Stojanovic, Andjelkovic-Apostolovic, Miolosevic, & Ignojatovic, 2018). Understanding the difference between each test and when it should be used is important in research when using statistical analysis.
Discuss the differences between non-parametric and parametric tests.
A parametric test is a statistical test of a hypothesis that is based on a population and built on a distribution which allows for the researcher to make generalizations about the stated population. A non-parametric is a statistical test where test necessitated is not metric or based on a population and its distribution because there is no information provided. The central tendency of a parametric test is the mean, while non-parametric is the median(Gray,2016).
Provide an example of each and discuss when it is appropriate to use the test.