testing in order to take into account multiple testing and multiple comparisons. This alpha adjustment has become particularly relevant in the context of
When it comes to exploratory studies or post-hoc analysis of existing data though
The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that.
The middle panel displays the same point estimates as in the left panel but with confidence intervals adjusted to account for a Bonferroni correction. The right
multiple f
When to Adjust for Multiple Comparisons. We make adjustments to control the FWER when: ○ We have more than one hypothesis or outcome (family of hypotheses)
multiple comparison adjustment
Therefore there are two ways for adjusting the statistical inference of multiple comparisons. First
PB R
Rothman (Epidemiology 1990;1:43-46) recommends against adjustments for multiple comparisons. Implicit in his recommendation.
Pruning is a common technique to avoid over-fitting in decision trees. Most pruning techniques do not ac- count for one important factor - multiple compar-.
KDD
would make multiple pairwise comparisons using t tests for mean difference in unpaired The test is frequently used with multiple-comparison adjustments.
(1968 1971) multiple-comparison adjustment to the standard statistical t-test to correct for the false- discovery bias inherent in multiple-comparison