Parametric and Non-Parametric Test

A specific statistical test is performed to generalise the test and define the probability ratio, which depends on probability distribution to reach the final value. The concluding value is concerned with the reasonability of the hypothesis related to different modes such as parametric and non-parametric statistical analysis. The test has specific methods for performing the test according to the present values and statistics. The classification of the parametric and non-paramedic place variable conclusions with similar specifications.

Parametric Test:

The parametric test is performed with the data parameters assumed with the population and data. The parametric test depends upon the continuous variable and quantitative information. The data used in a parametric test are measured on the ratio scale measurement, and all the derived values follow the standard distribution process.

In the parametric test, the value’s central tendency is considered as the mean of the distribution of data. Due to this, the statistical power is reduced, as the data is highly prone to outlier values, which can be skewed at any instance because of using central tendency as the mean of the distribution.

The parametric test is usually used in the continuous distributions of data such as weights and height of the species over time, temperature information, etc. the data derived from the parametric test attain very low versatility in certain applications due to using assumptions in specific parameters. The most common parametric test is:

• Pearson’s Rank correlation-test (sample size<30)
• Z test (sample size>30)

Non-Parametric Test:

The non-parametric test is the statistical analysis that does not depend on any assumptions of data distribution and associate parameters to analyse and derive the final value. The non-parametric test has data that is not appropriately distributed and is skewed. This test is also known as the distribution-free test because it lacks the proper distribution.

The non-parametric test is widely used on many platforms. Its data does not attain any assumption for the population values, skewed population, or minimal population ratio. This statistical analysis method is also used when the data is either ordinal or nominal.

Here, the median value is the central tendency value of the population. This testing method has high-end flexibility on the practical platforms, with the non-linear and non-clumped data. There are fewer chances for improper usage and misunderstandings in this test because of its simplified format and robust nature.

For the calculations which are in the ranking order (like feedback, ratings, review, etc.), the non-parametric test is highly suitable. These tests often lose their statistical stability for the extensive sample size data. The most commonly used non-parameter on variable practical platforms are:

• Kruskal-Wallis test
• Sign test
• Spearman rank correlation
• Mann-Whitney test
• Wilcoxon signed-rank test

The Difference Between Parametric and Non-Parametric Tests:

There are many differences between parametric and non-parametric tests based on their data, usage, and data flexibility. These differences vary in their usage on the practical platform. Below is the tabular information with a brief comparison with all the aspects of the parametric and non-parametric test:

Conclusion:

Here are the significant differentiated aspects of the parametric and non-parametric test. The tests are an integral aspect of analysing data on any given parameters. The values here vary according to the data distribution, which can be skewed or normal. Their application on the practical platform includes these values, which decide whether to derive the final value through parametric or non-parametric tests. The sample size is also an essential factor that decides applying a specific type of test. Thus, parametric, and non-parametric tests are alternatives to each other depending on specific values and their distribution.