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T-test and ANOVA which stands for "Analysis of Variance" are statistical methods used in the testing of hypothesis for comparison of means between the groups. The t-test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. Examples of use ANOVA: Retail stores are often interested in understanding whether different types of promotions, store layouts, advertisement tactics, etc. lead to different sales; Medical researchers are often interested in whether or not different medications affect patients differently; Environmental Sciences researchers are often interested in understanding how different levels of factors affect plants and wildlife. We use ANOVA as opposed to t-Tests to determine the mean differences because every time we conduct a t-test there is a chance that we will make a Type I error. This error is usually 5%. By running two t-tests on the same data we will have increased your chance of "making a mistake" to 10%. The formula for determining the new error rate for multiple t-tests is not as simple as multiplying 5% by the number of tests. However, if we are only making a few multiple comparisons, the results are very similar if we do. As such, three t-tests would be 15% and so on. These are unacceptable errors. An ANOVA controls for these errors so that the Type I error remains at 5% and we can be more confident that any statistically significant result we find is not just running lots of tests. I personally think the t-test is easier if I just need to compare two groups. But if I need to compare three or more groups, then ANOVA is more accurate and time-consuming. Reply