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Exact(6)
We used a Bonferroni correction to address multiple testing; thus, for this analysis, Cronbach's α < 0.017 was considered significant.
A Bonferroni adjustment was made to adjust for multiple testing, thus null hypotheses were rejected when P < 0.025.
Even after post hoc analyses (α/3 for multiple testing, thus significance when p < 0.017), the significant difference between linear SVR and MLR remained valid.
It may also be mentioned that the analyses were not adjusted for multiple testing, thus low P values should be regarded as interesting findings rather than conclusive evidence.
Although some adverse screening events are inevitable, the use of fecal immunochemical test for haemoglobin (FIT) would be one strategy to greatly reduce multiple testing, thus avoiding the adverse consequences resulting from weak-positive gFOBt results.
Although these findings suggest specific outcomes to examine in future studies, these statistically significant findings are not adjusted for multiple testing; thus, the overall type I error rate is inflated.
Similar(54)
We tested for and found no effect modification for NDI and all baseline variables in continuous and discrete models based on a P value of 0.01 to account for multiple tests; thus, we present only main effects.
The analysis shown in Figure 5 was corrected for multiple testing, and thus the corrected significance level (q<0.05) considers all tests performed in this part of our study.
Fibrinogen alpha chain peptides (ADpSGEGDFXAEGGGVR and ADSGEGDFXAEGGGVR) were found to associate with diabetes (p-value<0.05), albeit at levels that were not significant after correcting for multiple testing, and thus require future replication.
There were also no adjustments made for multiple testing and thus some associations may have been over-estimated.
In order to compensate for the multiple testing and thus to avoid imparting spurious significance, we used a stringent significance level of 0.002 for each test.
Related(14)
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