Sentence examples for multiple testing but from inspiring English sources

A SNP in GALNT1 (rs17647532) was the only one that remained statistically significant after Bonferroni adjustment for multiple testing but was not statistically significant in Hardy-Weinberg equilibrium among controls.

We chose not to correct for multiple testing but instead to validate our findings by using extreme value analysis.

This procedure corrects for multiple testing but accounts for correlation between markers, hence is less conservative than a Bonferroni correction, which is appropriate for independent tests such as unlinked markers.

These differences were not significant after adjustment for multiple testing but it is unlikely that this study was sufficiently powered for the very large number of tests carried out.

We performed our tests in a way that on the one hand did not ignore the problems of multiple testing, but on the other hand took into account the dependent and nested nature of the gene ontologies used.

Using the overly conservative Bonferroni correction the present data would not hold for multiple testing, but since the measures of insulin release during the OGTT are highly correlated we believe such corrections are too conservative.

In our experiments, we used a between-subjects design where both groups take multiple tests but the retention intervals are arranged such that one group takes its second test at the same lag at which the second group takes its first test.

We used multiple methods and multiple tests, but we used conservative rules to reduce the overall risk of false positive conclusions.

Only fish and mammals are significant if we correct for multiple tests, but overall there is a significant correlation for the remaining four groups if we combine probabilities (p = 0.0024).

One of them included testing of many pairwise interactions, and therefore multiple tests, but the authors corrected for them by proceeding in four selection steps in three different sample collections [ 22].

P-values were not corrected for multiple tests, but a Bonferroni correction would not exclude many indels since a large majority of them have P-values far below the cutoff (91% of all indels have P-values < 0.001, and 83% have P-values < 0.0001).