Question

ANOVA is useful because if you perform multiple t-tests: A. Type I error increases B. The...

ANOVA is useful because if you perform multiple t-tests:

A. Type I error increases

B. The risk of calculation error becomes unacceptably high

C. The risk of a Type II error is greater than 10%

D. It is not possible to determine which group(s) differed from one another since t-tests are omnibus tests of all means

Math   Statistics-And-Probability   
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Answer #1

Note: 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 two t-tests on the same data you 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 you are only making a few multiple comparisons, the results are very similar if you do. As such, three t-tests would be 15% (actually, 14.3%) and so on. These are unacceptable errors. 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.

Therefore, Anova is useful because if you perform multiple t-tests, Type I error increases.

Answer: A) Type I error increases.

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