On the meaning of measurement uncertainty

Grégis, Fabien

doi:10.1016/j.measurement.2018.09.073

Cited by 26 publications

(8 citation statements)

References 30 publications

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“…Conversely, two conflicting results about the same studied FIQ (for example, two calculated values of the Hubble constant, giving rise to a situation called the Hubble tension), measured using various methods with different classes of phenomena, indicate that the reliability of these results may need to be reviewed [44]. Thus, observation (the process of formulating the model) is a scientific problem, the possible solutions of which are realized by identifying previously unknown systematic errors, revising the original models, or discovering new theoretical knowledge [65].…”

Section: Discussionmentioning

confidence: 99%

Information Content of the Model for Calculating the Finite Precision of Measurements

Menin¹

2020

PSIJ

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Aims: We argue that the choice of a specific qualitative–quantitative set of variables in a model by a conscious observer fundamentally limits the achievable accuracy of the measurement process. Place and Duration of Study: Mechanical & Refrigeration Consultation Expert, between January 2020 and July 2020. Methodology: Using the concept of “finite information quantities” introduced by Gisin, we try to present it as a practical tool in science and engineering in calculating the proximity indicator of a model to the phenomenon being studied. Results: The formulated metric (comparative uncertainty) allows us to set the optimal achievable uncertainty of the model and to confirm the impossibility of implementing the principle of infinite precision. Conclusion: Any attempt to search for a universal physical theory must consider the uncertainty caused by the observer’s vision and the working of the human brain.

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Section: Discussionmentioning

confidence: 99%

Information Content of the Model for Calculating the Finite Precision of Measurements

Menin¹

2020

PSIJ

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“…-The IR radiation from the surroundings, which was reflected from the observed object (aluminum block) IRrefl, -The IR radiation emitted by the observed object IRobj, -The IR radiation emitted by air between thermographic camera lens and the observed object IRa, -The IR radiation emitted by additional close-up 2x lens IRl. All of the infrared radiation components, which were mentioned above, may be described by equations (1)(2)(3)(4).…”

Section: Components Of Ir Radiationmentioning

confidence: 99%

“…According to the definition, the uncertainty of measurement is a parametric (or parameters) characterizing how well the (essentially unique) true value of the measurand is believed to be known. The older definition specifies uncertainty as a non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used [2], [3]. The temperature measurement is also burdened with uncertainty [4].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of Thermographic Temperature Measurement with an Additional close-up Lens

Dziarski

Hulewicz

2021

Measurement Science Review

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The thermographic temperature measurement is burdened with uncertainty. This non-contact temperature measurement method makes it possible to measure the temperature of the electrical device under load. When the observed object is small (a few square millimeters) the spatial resolution of the thermographic cameras is often insufficient. In this case, the use of the additional macro lens is needed. After using an additional lens, the uncertainty of the thermographic measurement is different from the uncertainty of thermographic measurement without an additional lens. The values of the uncertainty contributions depend on the conditions during the measurement and the used methodology. The authors constructed an uncertainty budget of thermographic temperature measurement with an additional macro lens, based on EA-4/02 (European Accreditation publications). The uncertainty contributions were also calculated. On the basis of the calculated values of the uncertainty contributions, it was determined which factor had the greatest impact on the value of the thermographic temperature measurement with an additional lens.

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“…However, quantifying and representing error and uncertainty is a challenge for a wide range of analytical fields, and often what these concepts represent is not the same to all practitioners. This results in a spectrum of definitions that take into account the way truth, error, and uncertainty are conceived (Cross et al, 2017;Grégis, 2019;Kirkham et al, 2018;Mari et al, 2021). For atmospheric measurements assessing uncertainty is complex and non-trivial.…”

Section: Introductionmentioning

confidence: 99%

Air pollution measurement errors: Is your data fit for purpose?

Diez

Lacy

Bannan

et al. 2022

Preprint

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Abstract. When making measurements of air quality, having a reliable estimate of the measurement uncertainty is key to assessing the information content that an instrument is capable of providing, and thus its usefulness in a particular application. This is especially important given the widespread emergence of Low Cost Sensors (LCS) to measure air quality. To do this, end users need to clearly identify the data requirements a priori and design quantifiable success criteria by which to judge the data. All measurements suffer from errors, with the degree to which these impact the accuracy of the final data often determined by our ability to identify and correct for them. The advent of LCS has provided a challenge in that many error sources show high spatial and temporal variability, making laboratory derived corrections difficult. Characterizing LCS performance thus currently depends primarily on colocation studies with reference instruments, which are very expensive and do not offer a definitive solution but rather a glimpse of LCS performance in specific conditions over a limited period of time. Despite the limitations, colocation studies do provide useful information on measurement device error structure, but the results are non-trivial to interpret and often difficult to extrapolate to future device performance. A problem that obscures much of the information content of these colocation performance assessments is the exacerbated use of global performance metrics (R2, RMSE, MAE, etc.). Colocation studies are complex and time-consuming, and it is easy to fall into the temptation to only use these metrics when trying to define the most appropriate sensor technology to subsequently use. But the use of these metrics can be limited, and even misleading, restricting our understanding of the error structure and therefore the measurements’ information content. In this work, the nature of common air pollution measurement errors is investigated, and the implications these have on traditional metrics and other empirical, potentially more insightful, approaches to assess measurement performance. With this insight we demonstrate the impact these errors can have on measurements, using a selection of LCS deployed alongside reference measurements as part of the QUANT project, and discuss the implications this has on device end-use.

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On the meaning of measurement uncertainty

Cited by 26 publications

References 30 publications

Information Content of the Model for Calculating the Finite Precision of Measurements

Information Content of the Model for Calculating the Finite Precision of Measurements

Uncertainty of Thermographic Temperature Measurement with an Additional close-up Lens

Air pollution measurement errors: Is your data fit for purpose?

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