Mann-Whitney test performing

In case that both data sets to be compared are normally distributed the F-test is applied. The hypothesis of homogeneity of variance of both test series is eliminated when the significance level for homogeneity of variance is 5 %. The t-test for paired and non-paired data is performed when homogeneity of variance is present. In any case, a paired difference test (for paired data) or the U-test (for non-paired data) is likewise carried out (paired of difference test = Wilcoxon test U-test = Wilcoxon-Mann-Whitney or Mann-Whitney test, respectively). 

Figure 5.2 Comparison of the dose-AUC relationship of R-(-)-apomorphine (11), R-(-)-N- -propylnorapomorphine (80) and R-(-)-l l-OH-N- -propylnoraporphine (12). Data represent mean values S.E.M. of 4 animals. Statistical analysis by t-test p<0.05, p<0.01, p<0.001. For comparison with R-(-)-apomorphine (11) 30 nmol/kg equal variance test failed and than Rank Sum Test followed by Mann-Whitney test was performed.

The statistical procedures used in this thesis are summarized in this subsection together with hints for further literature. PASW Statistics 18 and Microsoft Office Excel were used for computations. T-tests and Mann-Whitney tests were performed to assess internal relationships between binary and continuous explanatory variables, particularly vehicle impact speed. Pearson and Spearman correlations were used to assess possible correlations among continuous variables. 

When we find a significant difference, we do not know which groups are different. It is not correct to then perform a Mann-Whitney U Test on all possible combinations rather, a multiple comparison method must be used, such as the distribution-free multiple comparisons. 

Parametric data were presented as mean SD. To determine differences in glutamate concentrations, a repeated-measures analysis of variance was performed. The cutaneous sensation, hind-limb motor function, and morphological changes of the spinal cord were analyzed with a non-parametric method (Kruskal-Wallis test) followed by the Mann-Whitney U-test. 

It has been advocated that the area under the ROC curve is a relative measure of a tesfs performance. A Wilcoxon statistic (or equivalently the Mann-Whitney U-Test) statists cally determines which ROC curve has more area under it. Less computationally intensive alternatives, which are no longer necessary, have been described. These methods are particularly helpful when the curves do not intersect. When the ROC curves of two laboratory tests for the same disease intersect, they may offer quite different performances even though the areas under their curves are identical. The performance depends on the region of the curve (i.e., high sensitivity versus high specificity) chosen. Details on how to compare statistically individual points on two curves have been developed elsewhere. ... 

By employing a statistical test method, e.g. f-test or Mann-Whitney U test [25], a P value can be computed for strictly assessing whether the mean of D is significantly different from zero (P<0.05) or not (P>0.05). If P<0.05, the sign of the mean of D is then used to compare which model (variable set) is of better predictive performance. If P>0.05, we say two models have the same predictive ability. 

Statistical Analysis. Data are presented as means SEM. Statistical comparisons between groups were performed using ANOVA. Fisher s Protected Least Significant Difference (PLSD) test was used to analyze the difference in lipid levels and the Mann-Whitney U-test was used for differences in atherosclerotic levels between dietary groups. 

The signed rank test described in the previous section is valuable for the study of single sets of measurements, and for paired sets that can readily be reduced to single sets. In many instances, however, it is necessary to compare two independent samples that cannot be reduced to a single set of data. Such samples may contain different numbers of measurements. Several non-parametric tests to tackle such problems have been devised. The simplest to understand and perform is the Mann-Whitney l/-test, the operation of which is most easily demonstrated by an example. 

A "comparison between groups," performed by the U test of Wilcoxon-Mann-Whitney. Only P values lower than 1% were considered statistically significant. 

Data processing ras performed using the IBM SPSS Statistics software version 22.0 for Windows operating system. The verification of research hypotheses it was used the statistical t-student test for independent samples, U of Mann-Whitney and Pearson correlation coefficient The interpretation of statistical tests ras carried out using a significance level of p-value < 0.05 with a confidence interval of 95%. 

All statistical computations were performed using GraphPad Prism version 4.0a for Mac OS X (GraphPad Software, San Diego, California, USA). Values of experimental groups are shown as mean SEM unless otherwise stated. One-way ANOVA with Tukey s post-test analysis was used to determine statistical significance. Where appropriate, either two-tailed t-tests or Mann Whitney U tests were performed. A probability of P < 0.05 was considered to be statistically significant. 

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