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In the short-path scenario, the differences between the metrics in terms of PDR are small.
In all three sites, the reduction of point density resulted, for most of the metrics, in significant differences between the metrics derived from the original and the thinned datasets.
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When the sample distribution for the difference between the corresponding metrics was not drastically different from normal, we also applied a two-sided one-sample t-test of the hull hypothesis that the mean value of the pair-wise difference between the metrics is zero.
In the end we sketch in some examples the differences between these metrics.
Finally, we discuss and highlight some of the advantages, limitations and key differences between the considered metrics.
This behavior highlights the differences between the two metrics.
The statistical significance analysis based on Pearson and RMSE shows that at 95% confidence level, there were no significant differences between the studied metrics.
The area under the curve (AUC) for each ROC was computed for each centrality metric and compared in all subjects using multiple pairwise t-tests with Bonferoni correction to test for differences between the centrality metrics.
In this study, we first examine the differences between the principal metrics used to calculate heterozygosity using long-term data from a marked population of Soay sheep (Ovis aries).
Alternatively, MS-SSIM computed in the PU space is another lower complexity substitute, as there is no statistical evidence to show performance differences between these metrics in terms of PLCC and SROCC.
Tables 1, 2 and 3 show the results of the two-sided Wilcoxon signed rank test for the null hypothesis that there were no statistically significant differences in medians between the metrics derived from the original and the thinned data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com