This content originally appeared on DEV Community and was authored by Roy
Key differences
The key difference between parametric and nonparametric tests is that the parametric test assumes data follows a specific distribution, like the normal distribution and relies on population parameters such as mean and variance. Whereas nonparametric tests do not depend on any distribution. Non-parametric does not make any assumptions and measures the central tendency with the median value. It is also used for non-normally distributed data or small sample data.
Definition of Parametric and Non-parametric data
Parametric Test Definition
In Statistics, a parametric test is a kind of hypothesis test which gives generalizations for generating records regarding the mean of the primary/original population. The t-test is carried out based on the students’ t-statistic, which is often used in that value.
The t-statistic test holds on the underlying hypothesis, which includes the normal distribution of a variable. In this case, the mean is known, or it is considered to be known. For finding the sample from the population, population variance is identified. 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 population distribution, which is meant by distinct parameters. It is also a kind of hypothesis test, which is not based on the underlying hypothesis. In the case of the non-parametric test, the test is based on the differences in the median. So this kind of test is also called a distribution-free test. The test variables are determined on the nominal or ordinal level. If the independent variables are non-metric, the non-parametric test is usually performed.
This content originally appeared on DEV Community and was authored by Roy
Roy | Sciencx (2025-10-09T15:18:11+00:00) Parametric and Non-Parametric tests. Retrieved from https://www.scien.cx/2025/10/09/parametric-and-non-parametric-tests-2/
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