Inferential Statistics for the Hypothesis Testing of Non-parametric Data

Non-parametric testing in inferential statistics for hypothesis assessment is often overlooked by researchers owing to the complexity of understanding and implementing the test. In this chapter, we have made an extensive attempt to integrate the statistical requirements along with the methodological details for implementing these tests in R. We have covered an extensive list of non-parametric tests starting from the Wilcoxon Signed Rank Test, the Wilcoxon Rank Sum Test (Mann-Whitney U-Test), Kruskal-Wallis Test, Friedman Test, Chi-Square Test, Fisher Exact Test, Fisher Exact Test with Monte Carlo Simulation, McNemar Test, and the Cochrane-Mantel-Haenszel Test. For each of these tests, we have systematically covered the assumptions to be made, the hypothesis tested, and the case scenario covered in a point-wise manner, starting from importing the data, checking the assumptions, computing accompanying descriptive calculations, performing the test, and finally interpreting the results. Information on things to note, as well as the development of appropriate graphical visualizations relevant to each one of the tests, have also been added. Finally, we have provided clinically relevant case scenarios for each test, which would help the readers easily understand and implement the same in their respective research.