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According to the normality of the distribution of the data (tested using a Lillefort's test) we used either parametric (e.g. t-test, ANOVA) or non-parametric (e.g. Wilcoxon, Kruskall-Wallis tests) statistical methods.
Given the normal distribution of the data, tested with Kolmogorov Smirnov tests, parametric tests were used.
Depending on the distribution of the data (tested with the Kolmogorov-Smirnov and Shapiro-Wilk) associations between QoL and other variables were analysed with t-test and one-way ANOVA, and correlations with Pearson and Spearman correlation tests.
Predictions were made on the common set of 79 complexes between our cleaned dataset, the data tested by Moal et al. (2011) and the CCHarPPI (Moal et al., 2015a, b ) pre-calculated data.
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According to the data test, the accuracy of this method can achieve 97 %.
All the data tests were repeated three times and expressed as the mean SD.
"I have a collaborator in Hawaii who is using the data testing engineering signal calibration.
Welch's two-sample t test was also applied to the data, testing for each probeset the null hypothesis of no difference between the case and control means.
Before performing the statistical tests, the data were tested for normality by the Shapiro-Wilk test.
Lastly, the paper presents new data testing the model's core hypothesis.
Normal distribution of the data was tested using Kolmogorox-Smirnov test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com