MODULE 8 - NON-PRAMETRIC TESTS Non-parametric tests, also known as distribution-free tests, are a category of...

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MODULE 8 - NON-PRAMETRIC TESTS Non-parametric tests, also known as distribution-free tests, are a category of statistical tests used when the assumptions of normality or equal variances required by parametric tests cannot be met. These tests are particularly useful when dealing with data that do not follow a specific probability distribution or when dealing with ordinal or nominal data. Non-parametric tests make fewer assumptions about the underlying data distribution, making them more robust and versatile in various research settings. One commonly used non-parametric test is the Wilcoxon signed-rank test, which is a non-parametric alternative to the paired t-test. It is used to compare the means of two related samples to determine if there is a statistically significant difference between them. Another well-known non-parametric test is the Mann-Whitney U test, which is analogous to the independent samples t-test but can be applied to data that do not meet parametric assumptions. Additionally, the Kruskal-Wallis test is a non-parametric alternative to the one-way analysis of variance (ANOVA) and is used to compare the means of three or more independent groups. Non-parametric tests are valuable tools in statistical analysis, providing researchers with options for hypothesis testing in situations where parametric assumptions are not met. They are widely employed in fields such as psychology, biology, and social sciences, as well as in practical applications where data distributions may not conform to parametric models, ensuring that statistical analysis remains robust and accurate. Module 8 Homework & Quiz You are required to write an essay on the use of non-parametric tests in statistical analysis. Instructions: In your essay, please address the following points: 1. Explain what non-parametric tests are and why they are used in statistical analysis. 2. Discuss situations where non-parametric tests, such as the Mann-Whitney U test, would be preferred over parametric tests, like the independent samples t-test. 3. Provide specific examples or scenarios where non-parametric tests are more appropriate and explain why. Evaluation: Your essay will be assessed based on clarity, depth of understanding, and the use of relevant examples and evidence. This assignment contributes significantly to your final grade, so put your best effort into it. Requirements: Format: APA style Page: At least 4 5 pages Reference MODULE 8 - NON-PRAMETRIC TESTS Non-parametric tests, also known as distribution-free tests, are a category of statistical tests used when the assumptions of normality or equal variances required by parametric tests cannot be met. These tests are particularly useful when dealing with data that do not follow a specific probability distribution or when dealing with ordinal or nominal data. Non-parametric tests make fewer assumptions about the underlying data distribution, making them more robust and versatile in various research settings. One commonly used non-parametric test is the Wilcoxon signed-rank test, which is a non-parametric alternative to the paired t-test. It is used to compare the means of two related samples to determine if there is a statistically significant difference between them. Another well-known non-parametric test is the Mann-Whitney U test, which is analogous to the independent samples t-test but can be applied to data that do not meet parametric assumptions. Additionally, the Kruskal-Wallis test is a non-parametric alternative to the one-way analysis of variance (ANOVA) and is used to compare the means of three or more independent groups. Non-parametric tests are valuable tools in statistical analysis, providing researchers with options for hypothesis testing in situations where parametric assumptions are not met. They are widely employed in fields such as psychology, biology, and social sciences, as well as in practical applications where data distributions may not conform to parametric models, ensuring that statistical analysis remains robust and accurate. Module 8 Homework & Quiz You are required to write an essay on the use of non-parametric tests in statistical analysis. Instructions: In your essay, please address the following points: 1. Explain what non-parametric tests are and why they are used in statistical analysis. 2. Discuss situations where non-parametric tests, such as the Mann-Whitney U test, would be preferred over parametric tests, like the independent samples t-test. 3. Provide specific examples or scenarios where non-parametric tests are more appropriate and explain why. Evaluation: Your essay will be assessed based on clarity, depth of understanding, and the use of relevant examples and evidence. This assignment contributes significantly to your final grade, so put your best effort into it. Requirements: Format: APA style Page: At least 4 5 pages Reference