A nonparametric test
Discussion #1
A parametric test is a test done when there is a normal distributed population (Gray, Grove, & Sutherland, 2017). A nonparametric test is a test that does not have interval level data and a normal distribution (Gray, Grove, & Sutherland, 2017). An example of a parametric test would be a t-test. A t- test is typically used to compare two independent groups that have means of normally distributed dependent variables. An example of a non-parametric test would be a Friedman’s test. This this would be appropriate when ranks will be used to determine the measurement. For example, if an allergist were testing an allergy, he would place 4 patches on several different people and determine the outcome by ranking which patch had the most reactions to the patch that had the least reactions.
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.
Discussion #2
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.
A T-test is an example of a parametric test. This test would be utilized when knowledge of the population is given or understood to be normally distributed. A Wilcoxon Signed-rank test is an example of a non-parametric test, and it would be utilized in situations that no prior knowledge of the population is normally distributed(Gray,2016).
Discuss the assumptions that must be met by the investigator to run the test.
The investigator must whether or not they can assume that the population meets the criteria for a normal distribution. If it can not be assumed, then a non-parametric test will be utilized(Gray, 2016).
