Averaging Results with Theoretical Uncertainties
F. C. Porter
TL;DR
This paper discusses the challenges of combining measurements with theoretical uncertainties and introduces an algorithm that treats these uncertainties as bias estimates to improve statistical validity.
Contribution
It proposes a novel algorithm that interprets theoretical uncertainties as bias estimates, providing a clearer statistical framework for combining measurements.
Findings
The algorithm offers a more consistent way to incorporate theoretical uncertainties.
It improves the reliability of combined measurement results.
The method clarifies the statistical interpretation of theoretical uncertainties.
Abstract
Combining measurements which have "theoretical uncertainties" is a delicate matter, due to an unclear statistical basis. We present an algorithm based on the notion that a theoretical uncertainty represents an estimate of bias.
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Taxonomy
TopicsScientific Measurement and Uncertainty Evaluation