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Cluster-based analysis methods in neuroimaging provide control of whole-brain false positive rates without the need to conservatively correct for the number of voxels and the associated false negative results.
If you deviate from the "conservatively correct" prefab platform -- you are out.
We used the Bonferroni method to conservatively correct for multiple testing.
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The supporting data analyses were conservatively corrected for the effects of multiple comparisons to control possible Type I error.
This is true even though all P-values have been conservatively corrected for multiple testing according to Bonferroni's method.
However, these associations would not be considered statistically significant after conservatively correcting for the total number of genes analyzed across the region, where P for significance = 5.0 × 10−3 (0.05/10−3
We added the "failed analyses" section and the more conservatively corrected threshold in Figure 6 to try to be upfront about this.
Analyses were performed in STATA [ 19] and as our sample included individuals from the same families, statistical analyses were conservatively corrected for the non-independence of observations using the 'robust' cluster command as is standard in analyses of this type (see [ 20, 21] for more information).
(The significance threshold was set to 95% of peak-level over space and time from 100 random category label shuffles, to correct conservatively for multiple comparisons).
The study then sought to conservatively extrapolate the findings, correcting only for sites that are home to heavy oil.
The Bonferroni correction is a simple way to correct for multiple comparisons (albeit conservatively).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com