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Exact(29)
This study was exploratory and we did not adjust for multiple comparisons because we did not want to increase type II failures on the cost of reducing type I failures [44, 45].
No adjustment was made for multiple comparisons because each SNP was selected with an a priori hypothesis.
We did not adjust p-values for multiple comparisons because each individual t-test was considered a replicate test of our primary predictions.
There was no adjustment for multiple comparisons because all assessments were considered as separate tests.
We did not correct for multiple comparisons because of the exploratory nature of our analyses.
These studies better address statistical challenges related to multiple comparisons because more systematic methods are utilized.
Similar(31)
Bonferroni corrections for multiple comparisons (α/5; because of the 5 categories explored in Figure 2) were used to adjust P values of statistical significance.
Studies evaluating the effects of acute exposure to air pollution are subject to multiple comparisons bias because the associations are often assessed with multiple pollutants (e.g. SO2, NO2, CO, O3, PM10, and PM2.5) and different exposure periods (e.g. same day, 1-day lag, and cumulative day averages).
P < 0.01 was used for statistical significance because multiple comparisons were made and because a high level of power was available.
No adjustments for multiple comparisons were done because the study was exploratory and we wanted to avoid excessive type II errors and to avoid inappropriately testing of a less relevant universal null-hypothesis [36].
Furthermore no corrections for multiple comparisons were done because the study was exploratory, and we did not want to increase type II failures on the cost of reducing type I failures [36, 54].
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