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Cited by 64Scientific Measurement and Uncertainty EvaluationAdvanced Measurement and Metrology TechniquesSensor Technology and Measurement Systems

Measurement, models, and uncertainty

Giordani, Alessandro, Mari, Luca·2012·IEEE Transactions on Instrumentation and Measurement
DOI 10.1109/tim.2012.2193695
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Abstract

Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this aim, a general model of measurement is sketched, which gives the context to highlight the unavoidable, although sometimes implicit, presence of models in measurement and, finally, to propose some remarks on the relations between models and measurement uncertainty, complementarily classified as due to the idealization implied in the models and their realization in the experimental setup.

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Crossref · 10.1109/tim.2012.2193695

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  • Error methods are well established in laboratory medicine and are likely to be used for singleton internal quality control procedures and for proficiency testing for the foreseeable future and for quality assurance purposes because of their familiarity and proven value.

    “Error methods are well established in laboratory medicine and are likely to be used for singleton internal quality control procedures and for proficiency testing for the foreseeable future and for quality assurance purposes because of their familiarity and proven value.15,24”

    From Uncertainty in Measurement and Total Error (2017) · cites this paper as [15]

  • Revised versions of the GUM 5 and VIM 9 are likely to explicitly endorse both error and uncertainty approaches, including both frequentist and Bayesian statistical methods.

    “Revised versions of the GUM 5 and VIM 9 are likely to explicitly endorse both error and uncertainty approaches, including both frequentist and bayesian statistical methods.14,15,[96][97][98][99][100]”

    From Uncertainty in Measurement and Total Error (2017) · cites this paper as [15]

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Authors
Giordani, A., & Mari, L.
Affiliations
  • Università Cattolica del Sacro Cuore (IT)
  • University Carlo Cattaneo (IT)
ORCID
Mari, Luca
Venue
IEEE Transactions on Instrumentation and Measurement
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2012-08
Volume
61
Issue
8
Pages
2144-2152
ISSN
  • 0018-9456 (ISSN-L)
  • 1557-9662
DOI
10.1109/tim.2012.2193695
OpenAlex
W2079699097
Cited by
64
References
30
Citations by year
2026: 2 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 5 · 2020: 5 · 2019: 7 · 2018: 7 · 2017: 8 · 2016: 3 · 2015: 7 · 2014: 3 · 2013: 1
FWCI
4.02
Type
journal article
Language
en
Open access
closed
Locations
IEEE Transactions on Instrumentation and Measurement (publishedVersion)
Funders
  • Politecnico di Torino
  • Libera Università Carlo Cattaneo
  • Università Cattolica del Sacro Cuore
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Scientific Measurement and Uncertainty Evaluation
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced Measurement and Metrology Techniques
  • Physical Sciences › Computer Science › Computer Networks and Communications › Sensor Technology and Measurement Systems
Keywords
measurement, measurement uncertainty, idealization, experimental setup, measurement interpretation, measurement results

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