Non-Parametric Tests

Posted: August 26th, 2021

Non-Parametric Tests

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Non-Parametric Tests

In statistical analysis, the difficult concept I encountered is the non-parametric tests. These tests are defined as approaches that never require distribution to meet the necessary assumptions and criteria to be analyzed. Furthermore, non-parametric tests work perfectly with data that is not normally distributed. In other words, non-parametric tests are also known as distributed-free tests. Non-parametric tests can be applied to other kinds of data, like nominal and ordinal data. Hence, Non-parametric tests tend to be the only suitable solutions for such types of variables. As an alternative method to parametric tests like ANOVA or T-test, non-parametric tests can only be used if the underlying data fulfills specific assumptions and criteria(Non-parametric tests and their classifications – exploring your mind, 2020). However, if the information meets the necessary criteria and assumptions to perform parametric tests; then, it should be applied. 

With further examination into the concept, I realized that the data size sample is an essential criterion to either select or choose the appropriate statistical technique. When conducting a statistical analysis with a small data sample, one might not have ascertained data distribution since the distribution tests will be inadequate or insufficient outcomes(Non-parametric tests and their classifications – exploring your mind, 2020). However, parametric tests are used in case of large sample sizes. On the contrary, if the data size is very small, and it impossible to validate its distribution; hence, the use of non-parametric tests is the appropriate option.

Also, specific assumptions for a sample data need to be metbefore it is analyzed using parametric tests. For instance, the data must have a homogeneous population variance and normally distributed. Nonetheless, skewed distributions sometimes might be observed in some data samples. The skewness gives poor analysis when one uses the parametric tests since the mean is unused to measure central tendency and is affected by extreme values. Therefore, non-parametric tests are the best approaches to examine median represented distributions and skewed distributions. In summary, non-parametric tests include several models and methods such as the chi-square test, the Mann-Whitney U test, Friedman test, Wilcoxon Signed Rank test, and the Kruskal-Wallis test. While, the Mann-Whitney U test is applied in comparing the differences in two independent samples ofdata.

References

Non-parametric tests and their classifications – exploring your mind. (2020). Exploring Your Mind. https://exploringyourmind.com/non-parametric-tests -and-their-classifications/