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For the purpose of detecting two-locus models, an exhaustive regression-based approach with a conventional correction for multiple tests is more powerful than multi-stage regression conditioning on significant main effects [18].
These differences remain significant if the Bonferroni correction for multiple tests is used.
In our opinion, traditional Bonferroni correction for multiple tests is too stringent for that kind of dataset, and would lead to passing over of important results.
Conventional methods, like Sanger sequencing or qPCR, are singleplex tests and, as the number of targets increases, the amount of DNA required for multiple tests is a limiting factor.
Because the number of crosses in a family increases with the number of likely mutants, the major effect of adjusting for multiple tests is that it makes the declaration of different mutations easier for families with a smaller number of likely mutants and harder for families with many likely mutants.
The diagnosis of cerebrospinal TB is challenging mainly in consequence of the extremely low number of bacilli due to the limited volume of cerebrospinal sample usually available, its subdivision between multiple tests is often self defeating and the highest sensitivity is achieved using the whole sample for the culture in liquid medium only.
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For experiments where multiple tests were performed, p-values were adjusted using Bonferroni corrections.
Ultimately, Dr. Marcus said, he believed that progress in artificial intelligence would require multiple tests, just as multiple tests are used to assess human performance.
Multiple tests are designed to test a whole collection of null hypotheses simultaneously.
When multiple tests were conducted, Bonferroni-adjusted alpha levels were used for hypothesis testing.
Abnormal patients were compared to the normal patients; thus, multiple tests were performed.
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