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An Approach to Combining Results From Multiple Methods Motivated by the ISO GUM.

M S Levenson1, D L Banks1, K R Eberhardt1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-8120.

Journal of Research of the National Institute of Standards and Technology
|August 24, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a method for combining results from multiple sources to find a consensus value and its uncertainty. The approach, inspired by the ISO Guide to the Expression of Uncertainty in Measurement, offers a robust solution for data aggregation.

Keywords:
Bayesreference materialsuncertainty

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Area of Science:

  • Metrology
  • Statistical Analysis
  • Data Science

Background:

  • Combining data from diverse sources (methods, labs) presents challenges in establishing a reliable consensus value and uncertainty.
  • Existing methods may not fully address the complexities of inter-method variability and uncertainty propagation.
  • The ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM) provides a framework for uncertainty evaluation.

Purpose of the Study:

  • To discuss the problem of determining a consensus value and its uncertainty from multiple methods or laboratories.
  • To present desirable criteria for a robust solution.
  • To introduce and demonstrate a solution based on the ISO GUM framework and compare it with a Bayesian approach.

Main Methods:

  • A solution motivated by the ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM) was developed.
  • The proposed solution was applied in a detailed worked example.
  • A Bayesian hierarchical model was formulated and compared against the ISO GUM-motivated solution.

Main Results:

  • The ISO GUM-motivated solution provides a structured approach to consensus value and uncertainty determination.
  • The worked example illustrates the practical application and benefits of the proposed method.
  • Comparison with the Bayesian model highlights the strengths and potential differences in outcomes.

Conclusions:

  • The proposed ISO GUM-inspired method offers a practical and effective way to determine consensus values and uncertainties.
  • The worked example validates the methodology for real-world data aggregation scenarios.
  • Further exploration of Bayesian approaches in conjunction with GUM principles may yield complementary insights.