Sentence examples for underlying normality from inspiring English sources

P value was calculated by independent samples 't' test after confirming the underlying normality assumption.

P value was obtained by one-way analysis of variance (ANOVA) after confirming the underlying normality assumption.

Values of distance are shown as mean ± standard deviation, P values of which are obtained using independent samples 't' test after confirming the underlying normality assumption.

We used a maximum likelihood estimation procedure after checking the data to confirm they did not violate assumptions for underlying normality of the observed variables.

Although regression models of large sample size are robust to some degree of non-normality (Lumley et al., 2002), all the linear models were controlled for homogeneity and distribution of residuals to avoid violation of underlying normality assumption.

For example, we excluded tularemia and human rabies because any clusters of these diseases would be detected without automated analyses and because the underlying normality assumption of the method is violated for rare events.

The use of polychoric correlations required underlying bivariate normality for each pair of items [ 32].

The hypothesis testing was conducted using robust maximum-likelihood (RML) estimation because underlying approximate normality did not hold for some polychoric correlations (4% of RMSEA tests of close fit implied a rejection of underlying approximate bivariate normality at 5% level of significance).

These logistic regression models do not assume underlying bivariate normality between the exposure and the depressive outcome, as would be the case in structural equation models (SEM) based around polychoric correlations.

In this case, the EFA and CFA should be based on the matrix of polychoric correlations (rather than Pearson), which is also robust to underlying non-normality.

In such situations, it is now recognised that EFA and CFA should be based on the matrix comprising polychoric correlations, which is also robust to underlying non-normality.