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In addition to this, the WS test, comparing the ordered sample data with the expected value of the rank scores, or the normal scores or "rankits" [38], leads to the same conclusion.
In the present study, the WS test showed high specificity and gave PPV values with acceptable LRs when BDI, DRI, and pain scores were used as reference standards.
The ability of the WS test (i.e. specificity) to detect those patients without psychological distress was in the range of 84 88%; however, the sensitivity was poor (< 60%).
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Due to non-gaussian data distribution as determined by the Shapiro-Wilk's W test, group differences of endocannabinoid concentrations were tested using the Wilcoxon-Test.
The Shapiro-Wilk W test was used to assess normality.
A Monte Carlo study shows that the bootstrapped D-W test considerably outperforms the original D-W test and (a+bdU) approximation.
However, a more quantitative approach is possible using the Kendall W test of concordance [33].
The problem of comparing w test treatments with u controls in b blocks of size k(⩽w) each is considered.
The Durbin Watson (D-W) test is one of the most widely used tests for autocorrelation in regression models.
When bootstrap is utilized, however, a more powerful test than the bootstrapped D-W test can be designed.
Data were tested for normality using the Shapiro Wilk W test and for homogeneity of variance with the Levene test.
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