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To address multiple comparisons problem, we employed permutation, Friedman's and Wilcoxon's signed-rank tests where appropriate.
A solution to this problem, cluster analysis, applies the biologically-motivated knowledge of correlation between adjacent voxels in one or more dimensions of the dataset to correct for the multiple comparisons problem and detect true neurophysiological effects.
One of the main challenges for such frameworks is the multiple comparisons problem, where the large number of statistical tests performed within a high-dimensional dataset lead to an increased risk of Type I errors (false positives).
We used the Holm-Bonferroni method [8] to reduce false positives caused by the multiple comparisons problem.
This phenomenon, known as the "multiple comparisons" problem, can be partially addressed using the Holm-Bonferroni method [8].
Furthermore, false positives may result from the multiple comparisons problem, which is partially corrected by the Holm-Bonferroni method.
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(iii) Multiple comparisons Problems of multiple comparisons, or multiple testing, occur when considering the outcomes of more than one statistical inference simultaneously.
This approach allowed for increasing the test sensitivity based on the assumption of temporal continuity of the data, thereby avoiding a massive multiple comparison problem and resulting in continuous intervals.
(This threshold was set to t = 1.8, corresponding to p < 0.05 with 13 degrees of freedom; note that the selection of this threshold still leads to a conservative treatment of the multiple comparison problem since a permutation test is subsequently performed). Contiguous threshold-surviving edges are defined as a cluster.
Family‐based designs require specialized analytic methods but they have distinct advantages: They are robust to confounding and variance inflation, which can arise in standard designs in the presence of population substructure; they test for both linkage and association; and they offer a natural solution to the multiple comparison problem.
23There are formal ways to address the multiple comparison problem (see Schochet 2008,2008).
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