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Each set of samples was shown to follow a normal distribution by using a normality test (Kolmogorov-Smirnov test).

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Normal distribution was tested by using a Kolmogorov Smirnov normality test.

Confidence intervals (CI) were calculated using a normality approximation algorithm.

Confidence intervals were calculated using a normality approximation algorithm.

Therefore, in all ensuing analyses we further corrected for this departure from normality by using an inverse-normalization method.

Statistical analyses performed Nutrient intake data were adjusted to achieve normality by using a power transformation.

Continuous variables were expressed as the mean ± standard deviation and compared by using a Student t test after normality testing.

Residuals were plotted across dose to check for the deviation of the variance homogeneity assumption, and residuals were also checked for normality by using a quantile-quantile plot.

Our strategy for this issue is to prune irrelevant combinations, such as those in which the expression values of two genes are randomly distributed as in Figure 1a, by using a hypothesis test assuming the normality of given examples.

The detection engine attempts to work out what is important by using a process of continuous modelling to define normality.

Continuous variables were explored for normality of distribution by using a Kolmogorov-Smirnov test, and in case of nonnormal distribution were presented as median and interquartile range and analyzed with nonparametric tests.

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