Biostatistics is a field of study that applies statistical methods to biological

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1 Nonparametric Tests in Public Health Researches Student’s Name Institutional Affiliations Course Title Instructor’s Name Due Date
2 Nonparametric Tests in Public Health Researches Public health researches often involve the collection and analysis of data from populations with varying characteristics, making nonparametric tests an essential tool. These tests are particularly useful in cases where data do not meet strict assumptions of normality or equal variances, which is a common occurrence in public health research. The Wilcoxon signed- rank test, Kruskal–Wallis test, Mann–Whitney U test, and Friedman’s two-way analysis of variance are among the most commonly used nonparametric tests in biostatistical research, and each is tailored to address specific research questions and scenarios. Public health researchers can use these tests to analyze a wide range of data, such as survey responses, clinical trial results, and epidemiological data. By offering flexibility in selecting the most appropriate test for their specific needs, nonparametric tests have become a cornerstone of statistical analysis in public health research.. In this essay, I will compare and contrast four nonparametric tests commonly used in biostatistical studies: Wilcoxon rank-sum test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test. I will also provide examples of how public health researchers can use each test and compare them with parametric tests. Wilcoxon Rank-Sum Test The Wilcoxon rank-sum test is a nonparametric test used to compare two independent groups of data. The test determines whether the medians of the two groups are significantly different. For instance, public health researchers may use the Wilcoxon rank-sum test to compare the blood pressure levels of men and women. The null hypothesis would be that there is no significant difference between the medians of the blood pressure levels of men and women. The alternative hypothesis would be that there is a significant difference. The Wilcoxon rank-sum test is a nonparametric alternative to the t-test. The test does not require normality assumptions,
3 making it suitable for non-normally distributed data. The test is also more robust to outliers than parametric tests. Mann-Whitney U Test The Mann-Whitney U test is another nonparametric test used to compare two independent groups of data. The test determines whether the medians of the two groups are significantly different. The test is suitable for ordinal and continuous data. For example, public health researchers may use the Mann-Whitney U test to compare the efficacy of two different drugs in treating a disease. The null hypothesis would be that there is no significant difference between the medians of the effectiveness of the two drugs. The alternative hypothesis would be that there is a significant difference. The Mann-Whitney U test is also a nonparametric alternative to the t-test. The test does not assume normality, making it suitable for non-normally distributed data. However, the Mann-Whitney U test is less powerful than parametric tests when the data are normally distributed. Kruskal-Wallis Test The Kruskal-Wallis test is a nonparametric test used to compare more than two independent groups of data. The test determines whether the medians of the groups are significantly different. The test is suitable for ordinal and continuous data. For example, public health researchers may use the Kruskal-Wallis test to compare the cholesterol levels of three different diets. The null hypothesis would be that there is no significant difference between the medians of the cholesterol levels of the three diets. The alternative hypothesis would be that there is a significant difference.
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