The importance and efficacy of advanced parametric and non-parametric tests commonly used in health sciences research

Muhammad Ilyas

doi:10.53730/ijhs.v10nS1.15956

Authors

Muhammad Ilyas
ilyas.phdscholar@lincoln.edu.my
Lincoln University College, Petaling Jaya, Malaysia

Keywords:

T-tests, ANOVA, Pearson correlation, Wilcoxon’s Signed Rank test, Mann-Whitney U test, Kruskal-Wallis test, Spearman’s Rank Correlation, Chi-square test

Abstract

Background: Advanced parametric and non-parametric statistical tests are important tools in modern health sciences research because they allow researchers to correctly and methodically assess complicated clinical and epidemiological data. Parametric approaches are frequently used in clinical and biological research to compare therapy groups, investigate risk factors, assess illness outcome predictions, and track changes in patient health indicators over time. Non-parametric approaches are particularly useful in nursing, public health, psychology, and medical research where data may not be normally distributed since they do not necessitate rigid assumptions about population distribution. Parametric tests include t-tests, Analysis of Variance (ANOVA) and Pearson correlation while non-parametric tests include Wilcoxon’s Signed Rank test, Mann-Whitney U test, Kruskal-Wallis test, Spearman’s Rank Correlation, and Chi-square test that play their key roles and highlighting their fundamental importance in health sciences research. Objective: To evaluate and assess the importance and efficacy of advanced statistical tests. Approach: This article systematically examines and synthesizes the existing literature on the importance and efficacy of advanced statistical tests. Conclusion: Advanced statistical tests are indispensable in health sciences research as they improve analytical precision, support informed decision making, and ultimately contribute to better health outcomes and more efficient healthcare systems.

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