The phrase "adjustments of false" is not correct and does not convey a clear meaning in written English.
It seems to be an incomplete or incorrect expression, and without additional context, it is difficult to determine its intended use.
Example: "The adjustments of false assumptions led to incorrect conclusions."
Alternatives: "corrections of inaccuracies" or "modifications of errors".
Due to the severe effect of multiple comparison adjustments on false negative error rates and the exploratory nature of this research, such adjustments have been avoided to prevent this.
Experimental results show that our model performs better than the traditional-SVM and Decision-Tree approaches in the presence of infrastructure adjustments, in terms of false positive, false negative, attack detection and accuracy rate.
As the Bonferroni adjustment is notably conservative, the LD adjustment provides minimization of false positives.
An adjustment of the false discovery rate was made to account for the multiple hypothesis tests [ 125].
A SBL model was used to find the most likely candidate breakpoints for the copy number state, and the BE procedure was used to remove sequentially the least significant breakpoints estimated by the SBL model, allowing a flexible adjustment of the false discovery rate (FDR) [ 20].
The variance (or SD) of the estimators decreased when we allowed more markers for fitting with adjustments for false positives using fdr.
Differential gene expression was calculated using a dual filtering criteria; (1) an estimation of statistical significance through the Local Pooled Error test (LPE) [64] calculated using S-Plus Array Analyzer with an adjustment for false discovery rate calculation of p value of <0.05 (Benjamini Hochberg) and (2) a fold change (FC) limit of 1.5.
Differential gene expression was calculated using the dual filtering criteria of ≥1.5 fold change and statistical significance determined through the Local Pooled Error test (LPE) [ 22] with an adjustment for false discovery rate calculation of p value of ≤0.10 [ 23].
For the purpose of our study, we performed a Monte-Carlo based adjustment of the false-positive rate to formulate comparable conclusions across all the tests.
Following adjustment for false discovery a total of 682 taxa demonstrated significant differences between the two groups, all of which were more abundant in the high LGG samples (a complete list of taxa exhibiting significantly different abundance is provided in Supplementary Table, S2).
