Parametric and Nonparametric Tests: A Brief Review

Banda Gerald,Tailoka Frank Patson

doi:10.11648/j.ijsd.20210703.12

Abstract

Context: Statistics is the cornerstone of markets, businesses, policy makers and other sectors that require analysis and interpretation of data. From generation-to-generation, statistics has proved useful in everyday life, not only that it helps improving the quality of life through counting and record keeping, but it also allows people to predict the future events and to make their own analysis. Before making a conclusion, data should be collected, analysed and interpreted. Evidence Acquisition: In this study, the paper reviewed parametric and nonparametric tests. Researchers sampled some articles where parametric and nonparametric tests were used without considering assumptions. Results: In this study, researchers provided a review of parametric tests; namely, independent sample t-test and dependent sample t-test, and nonparametric tests; namely, Mann-Whitney U test and Wilcoxon signed-rank test. The formulae for calculating parametric and nonparametric tests have been provided in the study. Procedures on how to conduct Mann-Whitney U test and Wilcoxon signed-rank test in SPSS have been written in this article. Test of normality has been discussed in brief as a key component in analysing parametric and nonparametric tests. Conclusions: Most of the studies that have been carried out have not been considering assumptions when analysing data using either parametric tests or nonparametric tests. This study looked at parametric and nonparametric tests. In parametric tests, the paper looked at independent and dependent sample t-test, while in nonparametric test, the paper looked at Mann-Whitney U test and Wilcoxon signed-rank test.

Highlights

Markets, businesses, policy makers and other sectors require Mathematics and statistics, in particular
We can look at the significance differences between two groups that are either coming from the normal population or not
According to [3], Banda Gerald and Tailoka Frank Patson: Parametric and Nonparametric Tests: A Brief Review parametric test focuses on the sample coming from the normal population, while in non-parametric test, the sample does not come from the normal population. [3] indicated that the term ‘parametric’ comes from the characteristic of population. [3], further explained that non-parametric test can only be used when the assumptions of parametric test have been violated

Summary

Introduction

Businesses, policy makers and other sectors require Mathematics and statistics, in particular. Using statistics and looking at the number of people from different countries world over doing business with China, British Broadcast Corporation (BBC) predicted that Corona Virus would cause problems worldwide. The whole world was affected by Corona Virus, and thousands of people were dying every day. This is just a simple example of the importance of statistics. According to [1], there are two sets of statistical tests for comparing means of the population and these are: Parametric tests and non-parametric tests. Parametric and non-parametric tests focus on coming up with the conclusion about the population. We can look at the significance differences between two groups that are either coming from the normal population or not. Nonparametric test is mainly about signs and ranks [5]

Problem Statement

Test of Normality

Independent Sample t-test vs Mann-Whitney U Test

Conclusion