In: Statistics and Probability
Why is ANOVA more appropriate than multiple t-tests when comparing more than two groups?
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.