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Except where stated otherwise, statistical differences were assessed using two-tailed one-sample or two-sample t tests, with correction for multiple testing as indicated.
We did not adjust for multiple testing as, with the exception of adherence, we were not testing specific hypotheses about the impact of either form of prescribing, and the issue of multiple testing is less crucial in exploratory analyses.
After implementing the highly stringent Bonferroni correction for multiple testing, as much as seven QTL interactions remained significant at the 1% bi-dimensional genome-wide level, whereas 11 were significant at the 5% genome-wide level.
Next, a wide range of analysis parameters can be adjusted, such as normalization method (SWAN, Functional, Quantile, Noob or Illumina), quality control filtering based on detection p values, failed sample threshold, Q-value cutoff for multiple testing as well as genomic regions for testing.
The P-values shown are Benjamini corrected for multiple testing as reported by DAVID.
As we screened all frequency bands of spontaneous cerebrocortical activity for differences between HI, S and D, we used a randomization approach to adjust for multiple testing as previously described [22].
No adjustment was made for multiple testing as the secondary outcomes were exploratory in nature.
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We corrected p-values for multiple tests as previously.
Statistical significance between experimental groups was determined using the unpaired t test or ANOVA with Newman-Keuls multiple test, as appropriate.
Moreover, the knockin animals showed changes in alcohol intake and preference in multiple tests as well as increased alcohol-induced hypnosis.
A p value <0.05 was considered to indicate an important modifier for both multiplicative and additive interaction assessments, despite the multiple tests as interaction tests tend to be underpowered [ 22].
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