Parametric Test
Parametric Test
A parametric test is a test whereby the population is well known, and there is the assumption of parameters. The mean value is always utilized when calculating the central tendency in a parametric test. Parametric tests are widespread as they make research straightforward. In a subject like statistics, generalizations when creating records on the mean of a population are given using parametric tests. The test is considered a hypothesis test that assumes the primary allotments in particular data (Mariappan, 2019).
For a parametric test to be successful, a researcher must ensure that various validity conditions are met so that the results may be reliable. Therefore, a parametric test is more significant in statistical power compared to non-parametric tests. When an effect is present in any data, a parametric test will detect it.
Parametric tests usually assume normal value distribution (a bell-shaped curve). For instance, the height is a normal distribution that when you draw a graph of different people’s height, the graph’s curve would be bell-shaped. Experts refer to the distribution as Gaussian. It is appropriate to use a parametric test when the sample size is large. It is also suitable to use the parametric test when analyzing non-normal appropriations for data sets. A parametric test is also used when the mean accurately represents the center of the distribution. A parametric test is effective because it helps in making effective and efficient decisions.
An example of a parametric test is the standard T-test used to determine the significant differences between means of two distinct groups (Chavan & Kulkami, 2017). The T-test will then provide the basis of comparison between the test group and the control group. A parametric test may be appropriate when a researcher wants to determine the amount of money people spend seeking quality health care services. A parametric test leads to a rejection of H0.
