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T-test or f-test (ANOVA), Chi-Square, Mann-Whitney U test & more

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There are numerous times when the information collected from a real organization will not conform to the requirements of a parametric analysis. That is, a practitioner would not be able to analyze the data with a t-test or f-test (ANOVA). Presume that a young professional had read about tests—such as the Chi-Square, the Mann-Whitney U test, the Wilcoxon Signed-Rank test, and Kruskal-Wallis one-way analysis of variance—and wants to know when it is appropriate to use each test, what each test is used for, and why each would be used instead of the t-tests and ANOVA. How would you address this person's concerns?

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Solution Summary

The different types of tests and appropriate use of the tests are identified.

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For addressing this concern remember the following:
1. T-tests would not reveal an association between two variables, because the procedure for T-Tests must be conducted separately, meaning you shall conduct a test in order of variable preference and/or priority.
2. Correlation analysis can only be used with quantitative variables and typically measure the association between variables.
3. Crosstab is a helpful descriptive statistic that lists the options for one variable as rows and the options for the other variable as columns, making possible the tabulation of two variables at the same time and the comparison of categorical variables. Nevertheless, it is extremely important to determine the association between variables and their statistically significance.
4. Parametric analysis can be referred to when the researcher can check that two samples are normally distributed. Therefore, Non-Parametric Analysis differ ...

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