Anthony Nicholls Journal of Computer-Aided Molecular Design 2014 (Open Access)
Contributed by +Jan Jensen
Almost every computed number we report has an uncertainty and ...
This wonderful and very readable paper tells you how to compute the uncertainty in your uncertainties and what they mean. There will be a follow-up paper that will describe how meaningfully compare quantities for which such uncertainties have been computed. I can't wait.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Contributed by +Jan Jensen
Almost every computed number we report has an uncertainty and ...
... without an assessment of this uncertainty, or a description of how to estimate it, what we have really delivered is a report, not a prediction; “we did X, followed by Y, and got Z”.Of course we all know this and we faithfully report RMSD values, Pearson's correlation coefficient ($r$) and other measures of uncertainty. However, when was the last time you saw an uncertainty attached to these quantities? In others words, how likely is it that a future study would compute the same RMSD value for a different set of experimental values using my method? Or, my $r$ value looks great but do I have enough data points?
This wonderful and very readable paper tells you how to compute the uncertainty in your uncertainties and what they mean. There will be a follow-up paper that will describe how meaningfully compare quantities for which such uncertainties have been computed. I can't wait.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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