Evaluation of Measurement Data — Guide to the Expression of Uncertainty in Measurement (GUM)¶
Joint Committee for Guides in Metrology. (2008). Evaluation of Measurement Data — Guide to the Expression of Uncertainty in Measurement (GUM).
Cited by¶
10 citations across 10 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bias
- International metrology standards formalize this split: the JCGM (2008) Guide to the Expression of Uncertainty in Measurement separates the correctable systematic effect from the random component and prescribes calibration against a reference standard to remove the former.
This sourceSèvres: Bureau International des Poids et Mesures. Explicitly separates the correctable systematic effect (a measurement offset that does not average out) from the random component, and prescribes correction/calibration against a reference standard to remove the former.
- International metrology standards formalize this split: the JCGM (2008) Guide to the Expression of Uncertainty in Measurement separates the correctable systematic effect from the random component and prescribes calibration against a reference standard to remove the former.
- Calibration
- It names the systematic procedure of measuring deviation between a system's output and a trusted external standard, then adjusting the system to reduce that deviation to an acceptable range.
This sourceBureau International des Poids et Mesures. Authoritative metrology guide that explicitly separates systematic error (a measurement offset that does not average out) from random error and grounds the treatment of measurement uncertainty against a reference.
- It names the systematic procedure of measuring deviation between a system's output and a trusted external standard, then adjusting the system to reduce that deviation to an acceptable range.
- Central Limit Theorem
- The √n intervention is operational: averaging \(n\) repeated readings shrinks the random-error standard deviation as \(1/\sqrt n\), so a metrologist who needs to halve noise quadruples the sample count, and the diminishing return is read directly off the square root.
This sourceCombines many small independent error sources into a Gaussian uncertainty budget and uses 1/√n reduction of random error by averaging repeated readings.
- The √n intervention is operational: averaging \(n\) repeated readings shrinks the random-error standard deviation as \(1/\sqrt n\), so a metrologist who needs to halve noise quadruples the sample count, and the diminishing return is read directly off the square root.
- Impartiality
- The Guide to the Expression of Uncertainty in Measurement (JCGM, 2008) explicitly separates systematic error (a violation of impartiality) from random error, treating the absence of systematic offset as the substrate-furthest realization of the prime — no parties, no preferences, only a function from measurand to reading that must not drift as a function of irrelevant features such as which instance of the instrument is used or which operator runs it.
This sourceBureau International des Poids et Mesures. Explicitly separates systematic error (a measurement offset that does not average out — a violation of impartiality) from random error; treats the absence of systematic offset as a structural property of the measurement function rather than a virtue of the operator.
Supported in partVerified against the work's full text
Distinguishes random from systematic effects inside a measurement model, but this is the GUM's Supplement 1, not the GUM, and it says nothing of impartiality or operator-independence.
“where D is a quantity of which the average of the five indications is a realization, and d1 and d2 are quantities describing, respectively, the random and systematic effects associated with using the comparator.”
- The Guide to the Expression of Uncertainty in Measurement (JCGM, 2008) explicitly separates systematic error (a violation of impartiality) from random error, treating the absence of systematic offset as the substrate-furthest realization of the prime — no parties, no preferences, only a function from measurand to reading that must not drift as a function of irrelevant features such as which instance of the instrument is used or which operator runs it.
- Measurement
- The observer-frame and uncertainty envelope are explicit: the value is reported as \(25.40 \pm 0.02\) mm, with the budget decomposed into systematic parts (jaw wear, thermal expansion if the lab is off 20°C) and random parts (reading repeatability), and the bare "25.40" is meaningless without that envelope.
This sourceStandard for reporting a value with an uncertainty envelope decomposed into systematic (Type B) and random (Type A) components.
Supported in partVerified against the work's full text
The source supplies the uncertainty-budget principle (systematic parts and random parts) but nowhere states 25.40 ± 0.02 mm, jaw wear, a 20 °C thermal term, or reading repeatability.
“Usually these are presented in a table, the “uncertainty budget”.”
- The observer-frame and uncertainty envelope are explicit: the value is reported as \(25.40 \pm 0.02\) mm, with the budget decomposed into systematic parts (jaw wear, thermal expansion if the lab is off 20°C) and random parts (reading repeatability), and the bare "25.40" is meaningless without that envelope.
- Measurement Uncertainty and Observational Noise
- The structural separation between a system's true state and the observed or measured state, where the difference—noise—arises from instrument precision limits, observer error, random environmental variation, or systematic bias in the measurement apparatus, as JCGM (2008) classifies in the canonical Guide to the Expression of Uncertainty in Measurement.
This sourceJoint Committee for Guides in Metrology, BIPM. Canonical metrological framework: classifies uncertainty components by source (instrument precision, environmental variation, systematic bias) and by evaluation method (Type A statistical, Type B non-statistical).
- The structural separation between a system's true state and the observed or measured state, where the difference—noise—arises from instrument precision limits, observer error, random environmental variation, or systematic bias in the measurement apparatus, as JCGM (2008) classifies in the canonical Guide to the Expression of Uncertainty in Measurement.
Domain-specific¶
- Dimensionless Quantity
- Listed in the references but not attached to a specific claim.
Mechanisms¶
- Measurement Uncertainty Budget Table
- This structured, contributor-by-contributor accounting is the standard the metrology world formalized so that stated uncertainties are comparable across labs.
This sourceStandardizes contributor-by-contributor evaluation and expression of measurement uncertainty so results can be compared across laboratories.
- This structured, contributor-by-contributor accounting is the standard the metrology world formalized so that stated uncertainties are comparable across labs.
- Uncertainty Budget Sheet
- Combined per the standard method and expanded to a 95% coverage interval, the total uncertainty turns out to eat roughly 40% of the meter's allowed tolerance.
This sourceDefines standard combination of uncertainty components and expanded uncertainty for a stated coverage probability.
- Combined per the standard method and expanded to a 95% coverage interval, the total uncertainty turns out to eat roughly 40% of the meter's allowed tolerance.
- Uncertainty Budget Table
- The discipline that keeps it honest is the GUM framework — inventory sources exhaustively, include systematic terms, handle correlation explicitly, and state the coverage — so the interval reflects the measurement rather than the desired conclusion.
This sourceRequires an uncertainty budget to account for relevant uncertainty sources, systematic effects, correlations, and the stated coverage of the reported interval.
- The discipline that keeps it honest is the GUM framework — inventory sources exhaustively, include systematic terms, handle correlation explicitly, and state the coverage — so the interval reflects the measurement rather than the desired conclusion.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 10 citations: 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://doi.org/10.59161/JCGM100-2008E ×3
- https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf ×1
- https://www.bipm.org/en/committees/jc/jcgm/publications ×1
Registry ID ref:eb0122ccf92a · see in the full table