Traditional multiple-testing corrections, adjust p-values derived from multiple statistical tests to correct for the occurrence of false positives.
Multiple statistical tests indicated that these predictions were both unbiased and within the specified precision of the estimated regression equations.
Moreover, since we perform multiple statistical tests, some will have p-values ≤ 0.05 purely by chance, even if all null hypotheses are really true.
By means of multiple statistical tests, including geographically weighted regression, we analysed the effects of eight variables on the regional spatial variation of aboveground biomass.
To reduce the risk of type I errors where multiple statistical tests were run on different distance ratios measured on the same dataset, the significance limit (α ≤ 0.05) was adjusted downwards according to Šidák (1967).
Multiple statistical tests including the Ljung Box Q-test, Engle's test for heteroscedasticity, the Kolmogorov Smirnov test and the goodness of fit test were performed to show the validity of the proposed model.
Fisher's method (FM) [12] and variations of Fisher's method [14], [15], [16], [17] have been used extensively for combining results from multiple statistical tests.
The analysis adjusts for confounding factors including founder effects, linkage disequilibrium of host HLA types, co-variation in HIV codons and corrections for multiple statistical tests.
Individual gene analyses revealed that, after correcting for multiple statistical tests, age had a significant effect on expression of nine of the 25 genes.
To account for the problem caused by multiple statistical tests we used the Bonferroni adjustment to modify the significance criterion (α/k where k = the number of statistical tests) [40].
This set includes genes exhibiting the most consistent expression change (over 2-fold) in cancer versus control tissues, which were deemed to be differentially expressed genes with high reliability, derived through multiple statistical tests.
