Question

1. Compare and contrast the independent t-test and the analysis of variance. How are they similar and how are they different. 2. Come up with two hypothetical examples demonstrating this strategy or come up with "real-world" application using your professional life (i.e. how could you apply this in your work).

Answer 1

1. In the independent t-test, we have two samples where we need to test the difference in the dependent variable between these two groups. In Analysis of Variance, it is similar to t-test in the sense that we can check the difference in the dependent variable but the difference in both these tests is that in ANOVA, we have three or more groups. Another similarity between both these tests is that both are parametric tests which means that the population from which the sample is taken should follow normal distribution, random sampling of the data should be done etc.

One real world application where we could use t-test is that to check the difference between the mean salary between Males and Females working at the organization. The hypothesis will be:

Ho: The mean salary of Males and Females at 'X' organization are not significantly different. μ1 = μ2

Ha: μ1 =/ μ2

One real world application where we could use ANOVA is that to check the difference between themean compensation across different departments in 'X' organization. As there are always three or more departments in an organization like Finance, Marketing, Operations etc., we will use one-way ANOVA for the same. The hypothesis will be:

Ho: μ1 = μ2 = μ3 = μ4 (There is no difference in the mean compensation across different departments)

Ha: There is a significant difference in the mean compensation between atleast two of the groups