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Which is an example of a multiple comparison method?
For example, with three brands of cigarettes, A, B, and C, if the ANOVA test was significant, then multiple comparison methods would compare the three possible pairwise comparisons: These are essentially tests of two means similar to what we learned previously in our lesson for comparing two means.
When do you need to correct for multiple comparisons?
Conclusion: you should always correct for multiple comparisons if you do multiple comparisons, regardless of how you selected those comparisons. If they weren’t picked before seeing the data, you should correct for that in addition.
What do you need to know about multiple testing correction?
One very important thing to remember is that multiple testing correction assumes independent tests.
Do you need to do a t-test for each composite outcome?
In this case, multiple comparisons corrections are not needed; separate t -tests should be conducted at the α significance level for each domain composite outcome, and the null hypothesis of no treatment effect in at least one domain would be rejected if each composite impact is statistically significant.
How is p value used in hypothesis testing?
Essentially, hypothesis testing is a statistical method which computes the probability of the strength of evidence based on the sampled data for or against the null (i.e. no difference or no change) hypothesis, which is culminated in a single numeric, namely the P value.
How is α used in the multiple comparison problem?
Of these, α is perhaps most relevant to the multiple comparison problem. It is important to first have a thorough understanding of how α is used in hypothesis testing. If we find a difference in the mean pain scores across the two groups, does it actually mean anything?
What is the error rate for Type 1 hypothesis tests?
For a single hypothesis test at the α=0.05 level, the type 1 error rate is only 5%. There is only a 5% chance of erroneously rejecting the null hypothesis. For 2 hypothesis tests, however, the overall α becomes 0.10. The probability of erroneously rejecting at least 1 null hypothesis is 0.10.