Sentence examples for correction for continuous from inspiring English sources

2. p values were calculated with chi-square test for categorical variables, and t-test with Bonferroni post-test correction for continuous variables.

Between-group differences regarding socio-demographic variables were calculated using analysis of variance with the Bonferroni post hoc correction for continuous variables and χ test for categorical variables.

The baseline characteristics of the patients in the three groups were compared by ANOVA with post hoc analysis by Bonferroni correction for continuous variables and by χ test or Fisher's exact test for nominal variables as appropriate.

Unpaired t -tests with post hoc Bonferroni correction for continuous variables (p=0.005) and Fisher exact tests for categorical variables (gender) (p<0.01) were used to examine differences in characteristics between patients and healthy controls.

Comparisons between multiple groups were made using the one-way ANOVA test with Bonferroni's correction for continuous variables and the Chi square or Fisher's exact probability test for categorical variables.

Data were analyzed using an unpaired t-test with Welch's correction for continuous variables or the Mann-Whitney test (two-sided) and Fisher's exact test for categorical variables using the IBM SPSS software Ver. 22. P values less than 0.05 were considered significant.

We performed univariate analysis to examine differences in baseline characteristics between the 3 cognitive subgroups (MCI, mild AD and moderate AD), using analysis of variance (ANOVA) with Bonferroni correction for parametric continuous variables (as confirmed by normality testing) and Kruskal-Wallis test for nonparametric continuous variables.

Yates' correction for continuity.

Patient, tumour, and treatment variables were compared between the pCR and non-pCR groups using the χ and Wilcoxon rank sum test with continuity correction for categorical and continuous variables, respectively.

The distributions of clinical and histologic characteristics were examined and compared between groups using analysis of variance (ANOVA), with Bonferonni correction, for normally distributed continuous variables, the Kruskal-Wallis test for non-normally distributed continuous variables, and the chi-square goodness of fit test or Fisher's exact test, as appropriate, for categorical variables.

Results between the four phenotypes (normal, sarcopenia, obese, and SO) were analyzed using analysis of variance (ANOVA) with Bonferroni correction for all parametric continuous variables, and chi-square test for categorical variables, using the SPSS version 22 and STATA 13.0 statistical programs.