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For the purposes of controlling type I error, this is a desirable property.
In contrast with current practice only to control the Type I error, this method enables to balance the Type I and Type II errors according to a certain criterion or cost function and enables, through the AUC, to quantify our ability to discriminate between genes with and without actual differential expression in a specific data set and using a certain hypothesis test.
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This estimation allows evaluating the type-I error of this fit test.
As this exceeds the 5% conventional cut-off for the type-I error rate, this indicates that there is no significant evidence of enrichment for TLR/ CARD genes.
This may have resulted in an increased chance for Type I error in this secondary analysis.
This is very unlikely to be the cause of a type I error in this study and a true significant difference between cases and controls missed.
Control of Type I error for this multiple testing process was effected by applying the Bonferroni correction.
Also, the multiplicity was not adjusted to control the type I error of this study.
We found they all showed the desired power and type I error and this was not affected by random censoring.
Tests exhibiting inflated type I error under this scenario in excess of the first scenario can be considered as not appropriately accounting for locus length.
Two-sided tests will be used with Bonferroni-adjustment to maintain overall Type I error for this secondary aim at 0.05.
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CEO of Professional Science Editing for Scientists @ prosciediting.com