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T-tests are helpful when you are looking for a difference between two samples. The T-test compares the means and confidence intervals of two groups to see if two groups are statistically the same, known as the null hypothesis, or if they are different, called the alternate hypothesis. However, when a comparison between more than two groups is needed, the T-test becomes problematic. For example, if you were a researcher asking the question, "Is there a difference X between these five groups of children as a whole?" If you conduct individual T-tests on pairs of the five samples, you may reject the null hypothesis and state that there is a difference within the group of five because two of the groups show statistical differences. But when you look at all five groups, there is no difference in the five means. Some examples of situations where you would use ANOVA instead of T-test are: 1) Researchers are trying to determine whether there is a difference in vaccine effectiveness in decreasing the rate of infection for COVID-19 between all the available vaccines currently available. You would take data samples where the type of vaccine received is the factor, and the infection rate would be the response. If the P-value of the test is lower than the significance level, then there is a difference in vaccine effectiveness present within the group. 2) Retailers are trying to see how different store layouts affect sales; if more than 2 layouts are being tested, then ANOVA testing would provide data on whether or not store layout affects sales. 2) A gardener is trying to see if there is a difference in plant growth for a particular plant due to variations of times watered per week and the amount of sunlight exposure. Personally, T-tests seem easier to comprehend since the number of variables and groups are constricted, making it easier to understand that there is a statistical difference between the two groups. On the other hand, I understand the need and advantages of ANOVA testing since you often want to compare multiple group responses. You may also want or need to study the effects when more than one variable is present