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All possible pairs of variables were tested for associations by appropriate statistical tests.
Fourteen preoperative risk factors and nine postoperative outcome measures were analyzed by appropriate statistical tests, depending on whether the data were continuous or categorical.
The baseline characteristics of the study population were computed and compared by appropriate statistical tests (Pearson's χ test for proportions and Student t test for continuous variables) according to HS status.
Differences in demographic and clinical characteristics, as well in positive and negative perceptions between these two groups were assessed by appropriate statistical tests (as acknowledged in the relevant tables; p <0.05).
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Where indicated by the research question, variables were cross-tabulated and hypotheses tested by applying appropriate statistical tests.
A Kolmogorov-Smirnov test was performed to test the distribution of numerical data, followed by the appropriate statistical tests.
However, analysis of temporal trends should include assessing consistency of changes in the facilities under surveillance by using appropriate statistical tests.
Furthermore, by applying more appropriate statistical tests (examining ratios instead of absolute measurements of gene expression) they were able to significantly increase the correlation coefficients obtained in comparisons of the technologies.
By means of appropriate statistical tests (Student t test for unpaired data and Pearson's χ2 test), 11 variables were found to be significantly different between groups: age, male gender, weight, ejection fraction, recent myocardial infarction, serum creatinine level, serum bilirubin level, COPD, cerebrovascular accident, diabetes on medication, and lowest temperature on CPB.
Appropriate statistical tests (Wilcoxon rank sum, Fisher's exact test) are shown by their p-value.
Appropriate statistical tests were used in statistical analysis.
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