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When we tested more than two groups and the returned p-value was significant (p < 0.01), multiple pairwise comparisons were conducted and p-values were adjusted using Bonferroni's correction to avoid increases of type error I due to multiple testing.
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We assume that read count of ith gene can be decomposed into two components: true signal S i that directly derived from transcript expression, and background noise B i due to sequencing error or misalignment.
In addition, because this study was designed to evaluate the effects of EGFR polymorphisms on the risk of overall lung cancer, the stratification analyses according to age, gender, smoking status and tumor histology might have a type I error (due to multiple comparisons) and/or a type II error (due to the small number of subjects in the subgroups).
However, when there is only a single study for each of the three contrasts, the Bayesian random-effects method has zero type I error (due to the vague or non-informative priors for τ), and the rate of type I error by frequentist random-effects model was similar to the fixed-effect models.
Failure to account for impact between groups can lead to inflated type I error due to the confounding effect of impact (Finch 2005).
Multivariate analysis also protects against inflated type I error due to multiple tests, especially when DVs are correlated (which was the case here).
The genotype distribution of GSTT2 was significantly (p = 0.017) different between patients (n = 20) and normal controls (n = 16), but this was considered to be a type I error due to the small sample size, as our study using a larger sample size (over 200 of both groups) revealed no significant difference (Matsuzawa et al, submitted).
We calculated the false discovery rate (FDR), the estimated proportion of false discoveries made versus the number of real discoveries made at a given significance level, to control for type I error due to multiple hypotheses testing in associating the factors to disease status [20].
+ ; Bonferroni's correction was used to adjust inflation type I error due to multiple testing.
Bonferroni's correction was used to correct inflation type I error due to multiple testing.
To avoid a type I error due to multiple comparison, Bonferroni's correction was applied to the results.
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CEO of Professional Science Editing for Scientists @ prosciediting.com