For other outcomes measured at a single time point, a p value <0.05 was used to indicate significance and no adjustment was made for multiple comparisons due to the multiple comparisons considered.
The responses of the two predefined groups, pediatricians and pediatric intensive care unit nurses/RTs, were compared using the Chi-square statistic, with P ≤ .05 after Bonferroni correction for multiple comparisons considered significant.
We corrected the Mann-Whitney univariate analysis by using the Bonferroni test for multiple comparisons, considering a significant P-value of 0.0056.
In order to keep at minimum the probability to find a statistically significant difference purely by chance, the usual nominal level (P = 0.05) has been lowered to 0.016 by Bonferroni adjustment for multiple comparisons considering the 3 studied SNPs.
P-values less than 0.05, corrected by Bonferroni's method for multiple comparison, were considered significant.
Clusters of a minimum of 100 contiguous voxels, with a threshold of two-tailed P < 0.005 (corrected for multiple comparison), were considered to be significantly different.
However, when multiple comparisons were considered, then a lower p-value was considered significant based on a Bonferroni analysis - this is defined at the beginning of each section of the results.
The bond strength mean values were compared using two-way analysis of variance (ANOVA) and a Tukey's multiple comparison test, considering two factors (loading mode and type of bracket) and their interaction.
Experiments were carried out in triplicates, and statistical analyses were performed using Student's two tailed test in conjunction with Scheffe's test for multiple comparison statistics considering p < 0.05, P < 0.01, and P < 0.001 as statistically significant, very significant, and extremely significant values, respectively, whereas P > 0.05 is treated as statistically insignificant value.
The p-values, after Bonferroni correction for multiple comparisons, were considered significant in the combined linear and logistic regression analyses; p-values of <0.05 were considered significant in each population analysis (KoGES and KCMS).
To take into account the effect of multiple comparisons, we considered only cases where the significance level of P < 0.05 was reached in both ANOVA and regression for at least 11 consecutive samples.
