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For case C1, adjusting the critical value for the Cochran-Armitage trend test is proposed.
Where significant differences were found, individual comparisons were made between groups using the t-statistic and adjusting the critical value according to the Bonferroni method.
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The intermediate choice is based on simulations inside and outside N10 and uses both the simulations and the fitted distributions to adjust the critical value upwards as either may be underestimates.
We adjusted alpha for multiple comparisons using the Bonferroni method, adjusting the critical α value to 0.01.
We used the Bonferroni method to correct for multiple comparisons, adjusting the critical α value to 0.01.
Since 24 tests were performed in the main linear and logistic regression analyses, we adjusted the critical p-value accordingly using the Bonferroni correction (p-value = 0.05/24 = 0.0021 for statistical significance).
These very low counts for CNVRs resulted in low statistical power where only an extreme case control ratio could exceed the critical value (adjusted for multiple testing).
If the sampling is incomplete, the critical value must be adjusted.
The critical value of F was adjusted by Greenhouse-Geisser procedure after checking the data for sphericity, that is, the correlations among all combinations of trials were equal.
The weighted method adjusts the test statistic with given weights rather than the critical value.
While a p value of 0.05 will be the critical value for all analysis, the p value will be adjusted for subgroup analysis according to standard procedures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com