After the first split in the trees, chromosomal domain-related features were chosen by the models as being statistically significant, formulating rules which suggest that genes located in large LL1 domains are less susceptible to iXist-mediated silencing (and vice versa for genes located in large HL1 domains).
Even though none of the associations we found were highly statistically significant, we cannot rule out that one or more of these SNPs, or alternative SNPs within the candidate genes we analysed were associated with ovarian cancer risk.
While associations remained statistically significant, we cannot rule out possible residual confounding by current BMI.
Although we find positive and statistically significant results, we cannot rule out that our findings are spurious and alternative scenarios are also at play.
However, this result was not statistically significant and we cannot rule out either that valerian is actually worse than placebo, or that the NNT could be as small as 6, although this appears unlikely.
Although the effect in the patients with stable disease was smaller and not statistically significant, we cannot reliably rule out a potential benefit, due to the smaller sample size.
Although this negative relationship is not statistically significant (P = 0.19), it rules out the possibility that unequal states might simply have greater species turnover than more equal states.
This clearly shows the prediction rule was robust; even though a key variable was missing, when tested on a new data set, as the prediction rule remained statistically significant even though the AUC values were reduced.
This rule is statistically significant (p < 0.01, with the null hypothesis of binomial distribution with probability of occurrence of the ageing-related class = 0.23 (relative frequency of this class in the dataset)).
The proposed analytical model is based on Rough Sets theory and identifies decision rules which give statistically significant association between product pedigree and repair actions.
