Difference Between Parametric and Non-Parametric Test
Parametric is a statistical test which assumes parameters and the distributions about the population is known. It uses a mean value to measure the central tendency. These tests are common, and therefore the process of performing research is simple. Non-parametric does not make any assumptions and measures the central tendency with the median value. Some examples of Non-parametric tests are Kruskal-Wallis, Mann-Whitney, etc.
Parametric Test Definition
In Statistics, a parametric test is a kind of the hypothesis test which gives generalizations for creating records about the mean of the original population. A t-test is carried out based on the t-statistic of students, which is often used in this value.
The t-statistic test holds on the underlying hypothesis that there is the normal distribution of a variable. Here, the mean is known, or it is taken to be known. For finding the sample from the population, population variance is determined. It is hypothesized that the variables of concern in the population are estimated on an interval scale.
Non-Parametric Test Definition
The non-parametric test does not require any distribution of the population, which are meant by distinct parameters. It is also a kind of hypothesis test, that is not based on the underlying hypothesis. In the non-parametric test, the test is based on the differences in the median. So, this method of test is also known as a distribution-free test. The test variables are determined on the ordinal or nominal level. If the independent variables are non-metric, the non-parametric test is usually performed.
Key Difference Between Parametric And Non-parametric
|Value for central tendency||Mean value||Median value|
|Population knowledge||Requires||Does not require|
|Used for||Interval data||Nominal data|
|Applicability||Variables||Attributes & Variables|
|Examples||t-test, z-test, etc.||Kruskal-Wallis, Mann-Whitney|