BIO 500 GC Differences between Parametric and Nonparametric Tests
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
Population and Sample
Mean, Median, and Mode
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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
What is the difference between a parametric and a nonparametric test?
Parametric tests assume underlying statistical distributions in the data. Therefore, several conditions of validity must be met so that the result of a parametric test is reliable. For example, Student’s t-test for two independent samples is reliable only if each sample follows a normal distribution and if sample variances are homogeneous.
Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met.
Parametric tests often have nonparametric equivalents. You will find different parametric tests with their equivalents when they exist in this grid.
What is the advantage of using a nonparametric test?
Nonparametric tests are more robust than parametric tests. In other words, they are valid in a broader range of situations (fewer conditions of validity).
What is the advantage of using a parametric test?
The advantage of using a parametric test instead of a nonparametric equivalent is that the former will have more statistical power than the latter. In other words, a parametric test is more able to lead to a rejection of H0. Most of the time, the p-value associated to a parametric test will be lower than the p-value associated to a nonparametric equivalent that is run on the same data.