Comparisons between two independent groups of numerical data were performed using the non-parametric Mann Whitney test.
Feature extraction was done for exercising Statistic Measured Index method to evaluate the distance between two independent groups by directly addressing the quality of signal.
The corresponding necessary numbers of subjects in a study aiming at detecting a 20% exposure difference between two independent groups of equal size were 154, 78, and 68, respectively (p≤0.05, power 0.80).
The Mann–Whitney nonparametric test was used to compare variables between two independent groups.
This model can be considered an extension of the Hotelling's in which there are indirect network externalities between two independent groups of consumers: users and developers.
This test is used to compare differences between two independent groups when the dependent variable is ordinal and not normally distributed.
Comparisons between two independent groups were made using t tests with a two-tailed confidence level of P<0.05.
Differences between two independent groups were tested with the ANOVA.
Differences between two independent groups were analyzed by the Mann–Whitney test.
Comparisons between two independent groups were analyzed by the Mann–Whitney U test.
Z-test was used for estimating the relative risk between two independent groups [ 28, 29].
