Suppose you perform a one-way ANOVA and the F value allows you to reject H0. Wha

Question

Suppose you perform a one-way ANOVA and the F value allows you to reject H0. What is the appropriate conclusion based upon that result?

d)Pairs of population means are different if the difference between the corresponding sample means is larger than the F value.

Suppose you are analyzing data from a study with 6 groups. Although you could perform 15 independent samples t-tests to do all possible pairwise comparisons, this would be a bad choice because:

Explanation / Answer

Suppose you perform a one-way ANOVA and the F value allows you to reject H0. What is the appropriate conclusion based upon that result?

a) At least some of the population means are different.

Suppose you are analyzing data from a study with 6 groups. Although you could perform 15 independent samples t-tests to do all possible pairwise comparisons, this would be a bad choice because:

c)there would be a high probability of making a Type I error.

Every time a t-test is conducted, there is a chance that of making a Type I error. This error is usually 5%. By running two t-tests, the chance increases 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.

a) At least some of the population means are different.

Suppose you are analyzing data from a study with 6 groups. Although you could perform 15 independent samples t-tests to do all possible pairwise comparisons, this would be a bad choice because:

c)there would be a high probability of making a Type I error.

Every time a t-test is conducted, there is a chance that of making a Type I error. This error is usually 5%. By running two t-tests, the chance increases 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.

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