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The data was analysed using SPSS IBM SPSS Statisticss, version 20) using a Mann–Whitney U test as data was not found to be parametric.
Complementary analysis comparing means used Kruskal-Wallis test as data were either asymmetrical or variances were not homogeneous.
HE4 and CA125 at each time point were compared using Wilcoxon rank test as data were not normally distributed.
When calculating differences in median time to departure by type of facility, we used the Kruskal-Wallis test as data were right-skewed.
The preoperative and follow-up Harris hip scores were compared with use of the Wilcoxon signed-rank test as data were not normally distributed according to skewness-kurtosis test.
The median rate of HO was obtained for both the patient populations and was compared with use of the nonparametric Wilcoxon signed-rank test as data where not normally distributed according to skewness-kurtosis test.
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Single unpaired comparisons were made with the Mann–Whitney U-test as data were not normally distributed.
This can of course be better tested as data for more species becomes available.
All these approaches generate information or hypotheses that can be rigorously tested as data become available from controlled experiments.
For example, at UCGS I gained experience with developing and validating quantitative and sequencing-based tests, as well as data analysis, variant interpretation and report writing.
For the statistical analysis of the neural growth cone results, the differences between groups were evaluated using the Kruskal-Wallis test (non-parametric) followed by Dunn's multiple comparison test, as the data were not normally distributed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com