Question

Provide examples of how the t test and ANOVA could be used to compare means within your work environment or domain of interest. Discuss the appropriateness of using the t test versus ANOVA.

Answer 1

T- test and the ANOVA are the two parametric statistical techniques used to test the hypothesis.

We know that there is A thin line of demarcation between t- test and ANOVA. Although both are based on the common assumption that the population from which sample is drawn should normally distributed.

When the population means of only two groups is to be compared, we use t-test.

But when means of more than two groups are to be compared, ANOVA is preferred

T- test examines whether the population means of two samples greatly differ from one another or not.

ANOVA is used when the comparison is to be made between more than two population means such as manufacturing defects from different shifts or from different process or from different plant.

We can say that t-test is a special type of ANOVA that can be used when we have only two populations to compare their means

Appropriateness of using the t test versus ANOVA is given below

We may use t- test for more than two means then

type one error increases each time. Because type one error is additive in nature. That is why to reduce type one error we use ANOVA. After using ANOVA we get F-statistic. Further more we can use Tukey HSD test to test the significant difference between the mean.

This is the brief difference between the t-test and ANOVA.