Difference Between Parametric And Non-Parametric Test

The key difference between parametric and nonparametric test is that the parametric test relies on statistical distributions in data whereas nonparametric do not depend on any distribution. Non-parametric does not make any assumptions and measures the central tendency with the median value. Some examples of Non-parametric tests includes Mann-Whitney, Kruskal-Wallis, etc.

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.

Definition of Parametric and Nonparametric Test

Parametric Test Definition

In Statistics, a parametric test is a kind of the 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.

What is the Difference Between Parametric And Non-parametric?

The key differences between nonparametric and parametric tests are listed below based on certain parameters or properties.

Properties Parametric Non-parametric
Assumptions Yes No
central tendency Value Mean value Median value
Correlation Pearson Spearman
Probabilistic distribution Normal Arbitrary
Population knowledge Requires Does not require
Used for Interval data Nominal data
Applicability Variables Attributes & Variables
Examples z-test, t-test, etc. Kruskal-Wallis, Mann-Whitney

These were the Difference Between Parametric And Non-parametric. To know more about each of them separately register with BYJU’S – The Learning App!

Frequently Asked Questions – FAQs

What is the benefit of using nonparametric test?

Nonparametric test do not depend on any distribution, hence it is a kind of robust test and have broader range of situations.

What is the benefit of using parametric test?

Parametric test is completely dependent on statistical data and have more chances of accuracy.

What central tendency value we consider for parametric and nonparametric test?

For parametric mean value is taken and for non-parametric test median value is taken into consideration.

What are the examples of parametric test?

T-test and Z-test are the examples of parametric test, in statistics

What are the examples of non-parametric test?

Kruskal-Wallis and Mann-Whitney

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