Parametric and Nonparametric Tests: A Brief Review

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.