Why is ANOVA more appropriate than multiple t-tests when comparing more than two groups?

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Let us assume that we are comparing three groups using multiple t-tests . For the given three groups, we need to run = 3 t-tests to evaluate mean differences among three groups. ANOVA test will be a single test to compare the mean differences of the three groups.
Every time you conduct a t-test there is a chance that you will make a Type I error. This error is usually = 5%. By running three t-tests the probability of making Type I error is the probability of making Type I error in at least one of the test = = 0.1426.

An ANOVA controls for these errors so that the Type I error remains at 5% and you can be more confident that any statistically significant result you find is not just running lots of tests.

The Type I error will significantly increases as we increase the number of multiple t-tests, but in ANOVA test the probability of making Type I error will be fixed at 5%. So, it is more appropriate to use ANOVA instead of multiple t-tests when comparing more than two groups.

orchestra answered 11 months ago

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