Sentence examples for multiple testing when from inspiring English sources

We used the false discovery rate (FDR) control to correct for multiple testing, when necessary [23], using the package "qvalue" in R v.2.8.1 software [24].

The closed testing procedure [43] was used to adjust for multiple testing when performing two group comparisons in a three-armed trial.

In order to avoid possible problems with multiple testing when analyzing high-density EEG data, we consistently relied on global randomization statistics that do not inflate the alpha-error.

The model with residual ancestral migration rate (m0∼10−10) and full replacement (δ = 1) clearly better fitted our data than any other model (Figure 3A, highest ψ1, the ψ1 of this model is significantly higher after correction for multiple testing when compared with the other ψ1 values, P<0.01).

The model that best fitted our data involves a substantial bottleneck among non-Africans (Figure 3C, 2≤βOoA≤20 giving the highest ψ1 and the only significant comparison after correction for multiple testing when all values of ψ1 are compared with the ψ1 of the constant size model, P<0.01), rejecting significantly a constant population size model for these populations.

Models involving an expansion at 25,000 50,000 years were those best supported by the data (Figure 3B, highest ψ1, the only significant comparison after correction for multiple testing when all values of ψ1 are compared with the ψ1 of the constant size model, P<0.01).

Indeed, Andolfatto et al. [33] corrected for multiple tests when they used a sliding window analysis to detect recombination.

To account for these multiple tests when determining allelic association to a trait, Sidak's correction was used (Sidak 1967).

Statistical analyses were performed using analysis of variance (ANOVA) followed by Newman-Keuls multiple test when appropriate.

However, clinicians usually do not rely on a single test score, but on the patients' performance on multiple tests when assessing cognition.

Thus, some inconsistency between the two datasets might come from a reduction of the statistical power by using the stringent FDR control for adjusting multiple tests when the multiple tests are not independent of each other [ 30, 31].