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Difference Between Parametric and Non-Parametric Tests

iacst-2022

Parametric tests

Parametric tests are a type of hypothesis testing method frequently used in statistics. Parametric tests create records of the original population about its mean. The parametric test results depend upon the number of samples or the sample space used to calculate the mean. Then, the parametric t-test is performed that depends upon the t-samples. The t-measurement is said to have an ordinary distribution of a variable. In a parametric test, the mean of a population plays a significant role, calculated or assumed to be of some value to writing a hypothesis.

Non-parametric tests

Non-parametric tests do not depend upon any hypothesis made earlier. It does not require any distribution in population to create a hypothesis. Unlike parametric tests, whose hypothesis value depends upon the mean, the value of the hypothesis depends upon the median in non-parametric tests. It is also called distribution-free testing. Test values are known at the average level. In short, if a parametric test is performed with independent non-metric variables, it is known as a non-parametric test.

Difference between parametric and non-parametric test

Properties Parametric Test Non-Parametric Test
Assumptions Yes, assumptions are made No, assumptions are not made
Value for central tendency The mean value is the central tendency The median value is the central tendency
Correlation Pearson Correlation Spearman Correlation
Probabilistic Distribution Normal probabilistic distribution Arbitrary probabilistic distribution
Population Knowledge Population knowledge is required Population knowledge is not required
Used for Used for finding interval data Used for finding nominal data
Application Applicable to variables Applicable to variables and attributes
Examples T-test, z-test Mann-Whitney, Kruskal-Wallis

Types of parametric and non-parametric tests

Parametric Tests for Means Non-Parametric Test for Medians
1 - sample t - test 1 - sample Wilcoxon, 1 - sample sign
2 - sample t - test Mann - Whitney Test
One - way ANOVA Kruskal- Wallis, Mood’s median test
With a factor and a blocking variable - Factorial DOE Friedman Test

Advantages of parametric and non-parametric testing

1. Individuals can choose their testing method depending upon the type of data they have.

2. If the distribution is small and normally distributed, one may go with parametric testing.

3. Parametric tests help to analyze large data with non-normal distributions.

4. Non-parametric tests are used to analyze conclusions that are harder to achieve.

5. The tests are performed without wasting much time.

6. These tests are straightforward and use simple statistical tools to lay down the hypothesis.

Disadvantages of parametric and non-parametric testing

1. The final response depends upon the statistical measurements like mean and median. If the data sample is incorrect, the hypothesis may be incorrect.

2. Parametric tests only work when the data sample is large. The more the data, the more efficient the hypothesis will be.

3. If you have small data, you are compelled to use non-parametric testing and median for calculations.

4. If one uses the non-parametric tests, the chances of the hypothesis working or getting recognized are lowered compared to parametric testing.

5. At times, non-parametric testing may support parametric testing, but the opposite is not valid.

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