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Ala Bazyleva works on chemical thermodynamics and the quality of thermophysical-property data. Her named profile at the National Institute of Standards and Technology describes research involving measurements, thermodynamic modeling and critical data assessment.^[ref]

She is corresponding author and task-group chair of a 2021 technical report proposing seven reference binary systems for liquid-liquid equilibrium studies. The work evaluates existing measurements, recommends property values with measurement uncertainty, and provides equations for comparison within stated experimental ranges. Its contribution is a defensible reference for checking instruments and methods, rather than treating any previously published number as equally reliable.^[ref]

An original comparison illustrates the role of uncertainty. Suppose a reference mole fraction is 0.200 with standard uncertainty 0.002, and an independent measurement gives 0.206 with standard uncertainty 0.001. If those uncertainties describe independent contributions, the standard uncertainty of their difference isThe observed difference 0.006 is about 2.68 times that scale. Comparing only the central numbers would miss how strongly the result depends on the reference and measurement uncertainties. This stipulated diagnostic is not a universal acceptance threshold or a numerical result from Bazyleva's report.

A useful experimental check also matches temperature, pressure and composition conventions to the reference conditions. A precise instrument can disagree with a reference because it examined a different state, so agreement tests need both a defined measurand and a supported uncertainty statement.

Analytical sampling uncertainty is the contribution to measurement uncertainty arising when collected material is used to infer a property of a specified sampling target. The target might be a bulk lot or a defined location and time interval. Its composition need not equal that of one convenient portion. Sampling and physical preparation therefore belong in the measurement model when the measurand concerns the larger target.^[ref]

A duplicated experiment can separate two random components. Within each target, collect two independent samples by the stated protocol, prepare them separately, and analyse each twice. For a simple model,where $i=1,2$ labels collection and $j=1,2$ labels analysis; all random terms are centered and independent except for the shared $S_i$ within a sample. The within-sample difference has variance $2{\sigma }_a^2$. The difference between the two sample means has variance $2{\sigma }_s^2+{\sigma }_a^2$. Repeating this design across targets supports estimation of both components; different target means cancel in each within-target difference.^[ref]

For original stipulated difference standard deviations2and5milligrams per kilogram, respectively,in squared concentration units. One analysis on one sample has combined standard deviation $\sqrt{13.5}\approx 3.67$milligrams per kilogram. Averaging two analyses of that same sample gives $\sqrt{11.5+2/2}\approx 3.54$; averaging one analysis from each of two independent samples gives $\sqrt{(11.5+2)/2}\approx 2.60$. More laboratory replication and more independent collection address different contributions.

A collaborative wheat-and-coffee trial varied the sampler as well as the collected material. Its contrasting reproducibility and repeatability findings show why the uncertainty depends on which collection conditions are allowed to vary.^[ref]

Duplicate sampling with one sampler and protocol does not establish absence of a shared sampling bias. Reference comparisons, alternative protocols or inter-organisational trials can test additional effects. Common preparation losses or measurement calibration errors also require covariance terms beyond the independent model.

An assigned value in proficiency testing $x_{\mathrm{pt}}$ is the benchmark attributed to the proficiency-test item for evaluation of participant results. Its origin, measurement uncertainty and relationship to the specified measurand are part of its meaning. It is not automatically a true value known without uncertainty.

The guide distinguishes assignment by formulation, a certified reference material, results from one laboratory, an expert-laboratory consensus or participant consensus. These methods provide different evidence and dependencies; a simple interlaboratory consensus value is only one possibility.^[ref]

For an original formulation example, suppose the relevant mass concentration is analyte mass divided by final solution volume:The assigned value in this stipulated complete-transfer model is $c=2.00mg/L$. If $u(m)=0.02\mathrm{mg}$ and $u(V)=0.005\mathrm{L}$ are independent standard uncertainties, first-order propagation givesso $u(c)\approx 0.0224mg/L$. Purity, incomplete transfer, stability or nonuniform mixing would require additional model terms. Formulation alone may not reproduce extraction or preparation difficulties encountered in routine samples.^[ref]

Participant consensus introduces a different dependency. If four laboratories report 100 and a fifth reports 110, their arithmetic mean is 102. The fifth laboratory's deviation from this benchmark is 8, whereas its deviation from the other four laboratories' mean is 10. In general,where ${\bar{x}}_{-i}$ is the arithmetic mean excluding participant $i$. This original identity shows participant influence on a consensus assigned value; the same result being assessed helps determine its comparator. It does not imply that every scheme should use a leave-one-out rule, but the dependence must not be mistaken for an independent reference comparison.

The provider should record how the assigned value was obtained and how its uncertainty was evaluated. A precise participant consensus can retain shared bias in an interlaboratory consensus, while an independent reference can itself have substantial uncertainty. Those are distinct limitations requiring different evidence.

Ian Mann is a metrology and laboratory-quality specialist credited with Brian Brookman as co-editor of the 2021 third edition of Selection, Use and Interpretation of Proficiency Testing (PT) Schemes by Brian Brookman and Ian Mann. The editorial page identifies him with SAS in Switzerland. This identifies the proficiency-testing author without attributing the careers of other people named Ian Mann to him.^[ref]

The guide connects proficiency testing with practical decisions about laboratory measurements. Its selection criteria ask whether a scheme's material, measurand, concentration range and assessment approach fit the laboratory's actual work. A comparison using an unsuitable material can fail to test the procedure for which competence is being assessed.^[ref]

An original example illustrates why interpretation needs the assessment criterion. For a participant result $x=102$, assigned value $x_{\mathrm{pt}}=100$ and standard deviation for proficiency assessment ${\sigma }_{\mathrm{pt}}=1$ in compatible units, the proficiency-test z-score isA scheme using ${\sigma }_{\mathrm{pt}}=2$ would give $z=1$ for the same result. The result and its difference from the reference did not improve; the assessment scale changed. The chosen standard deviation for proficiency assessment must therefore be read with the score.

A participant's reported standard uncertainty answers another question: how uncertain is that participant's measurement result? It is not automatically the scheme's performance criterion. The guide separates evaluation scores, assessment spread and laboratory investigation of unexpected results.^[ref] Mann's documented editorial contribution is to this shared measurement-quality guidance; no separate invention or personal experimental result is implied.

Instrumental drift is a change of an instrument's indication or response with time while the relevant input is held fixed. A changing temperature can alter an electronic offset or the dimensions of a measuring frame; aging can alter its scale. A smooth drift can make repeated readings look precise while shifting the inferred measurand.

Interleaving a stable reference with the sample distinguishes a common changing response from a sample change. The ABBA measurement sequence cancels a common drift that is an affine function of time over the sequence; its derivation also shows why curvature in the drift survives. A reference measured only at the start cannot establish that the instrument stayed stable afterward. Residual drift contributes to measurement uncertainty rather than disappearing because a correction was attempted.

An interlaboratory consensus value is a value derived from results supplied by a specified group of laboratories to represent their common location for a stated measurand. It can serve as an assigned value in proficiency testing, but consensus and independent reference assignment are different ways of establishing that benchmark. The procedure must specify the participating results, estimator and associated measurement uncertainty.^[ref]

For an original example, three laboratories report $9.90$, $10.00$ and $10.10$ in the same units. Their arithmetic mean is $10.00$ and their sample standard deviation is $0.10$. Under a model of independent, equally distributed, unbiased laboratory errors, the estimated standard uncertainty of this mean is $0.10/\sqrt{3}\approx 0.0577$. This calculation does not include an unrecognized systematic error common to all three laboratories.

Choosing a location estimator is a substantive decision. For stipulated results $100.0$, $100.2$, $100.4$ and $105.0$, the arithmetic mean is $101.4$, whereas the median is $100.3$. Robust statistics can reduce the influence of an isolated extreme result, but cannot decide from these four numbers whether that result is erroneous or represents a different measurement method or material condition. The guide discusses robust approaches while distinguishing consensus assessment from fitness-for-purpose criteria.^[ref]

When laboratories supply valid, mutually uncorrelated standard uncertainties $u_i$ for unbiased estimates of the same quantity, an inverse-variance weighted mean usesThe last equality belongs to that model. Shared references, method-specific effects or underestimated $u_i$ can invalidate its interpretation. A laboratory reporting a small uncertainty should not receive overwhelming influence merely because that number was submitted.

An interlaboratory consensus value therefore summarizes a specified collection of evidence. It is not automatically the true value, a certified reference material value, or an independent test of those same laboratories. Shared bias in an interlaboratory consensus and the influence of participants on their own benchmark must be considered before interpreting a small deviation as evidence of accuracy.

The measurand is the quantity intended to be measured, specified with enough detail about the system, conditions and averaging convention to distinguish it from instrument output or a different derived quantity.

A measured quantity value is a value used to represent a measurement result. It may be obtained from one observation or calculated from replicate observations, for example as an arithmetic mean. The VIM distinguishes this value from a raw measurement indication and from the fuller result with its measurement uncertainty.^[ref]

For an original electrical resistance example, suppose three instrument indications are $100.04$, $100.06$ and $100.05\Omega$. A measurement calibration gives additive indication offset $b=0.02\Omega$, so the corrected values are $100.02$, $100.04$ and $100.03\Omega$. Their arithmetic mean is the representative measured quantity valueThis value is a numerical estimate of the specified measurand, such as the resistor's resistance at a stated temperature. It is not established as an exact true value merely by correcting the indications.

The three values have sample standard deviation $s=0.01\Omega$. If repeat deviations are independent, their contribution to the mean's standard uncertainty is $s/\sqrt{3}\simeq 0.0058\Omega$. Suppose the common correction has independent standard uncertainty $u(b)=0.02\Omega$. First-order propagation of uncertainty givesRepeating the readings reduces the first term under these assumptions; it does not repeatedly remeasure and average away the same common correction. These are illustrative uncertainty inputs, not observed equipment data.

Reporting the measured quantity value therefore requires its unit and measurand definition. Reporting the complete measurement result also identifies the associated measurement uncertainty, its convention and relevant conditions. A change from ohms to milliohms changes the numerical expression, while leaving the represented physical value unchanged.

measurement accuracy concerns closeness to the value of the specified measurand; high repeatability alone does not establish it. calibration, an adequate response model and assessment of residual systematic effects are required alongside statistical precision.

A measurement indication is the value supplied by a measurement instrument or measuring system. It may appear as a pointer position, displayed number, printed output or transmitted code. The indication and the corresponding measurand value need not be values of the same kind of quantity.^[ref]

For an original mechanical readout, suppose the indicated pointer position is a length $z$, related to input pressure $p$ byThe measurement indication $z=21.00\mathrm{mm}$ then corresponds to $p=(z-z_0)/S=100.0\mathrm{kPa}$ under this measurement model. The position is observable; the pressure value is inferred using the response relation. A printed pressure scale could carry out part of that conversion physically without changing the underlying distinction.

If the pointer position is uncertain by $0.10\mathrm{mm}$, that contribution corresponds to $0.50\mathrm{kPa}$ in this stipulated linear model. Uncertainty in the zero $z_0$, transducer sensitivity $S$, temperature response and instrumental drift can add further contributions. Closely spaced scale markings therefore do not establish the total measurement uncertainty.

Record the original indication and instrument settings when applying corrections. A corrected measured quantity value can change after improved measurement calibration even though the recorded indication remains the same. For example, revising the assumed zero from $1.00$ to $1.20\mathrm{mm}$ changes the inferred pressure from $100.0$ to $99.0\mathrm{kPa}$. The complete measurement result accompanies the inferred value with its uncertainty and relevant conditions; the raw measurement indication alone cannot supply that information.

A measurement model relates the measurand to measured inputs and correction quantities, for example $R=V/I$ when inferring electrical resistance. Its mathematical form states which physical effects are included in the result.

For small uncertainties, linearization gives $u_R^2\approx u_V^2/I^2+V^2{u}_I^2/I^4-2V\mathrm{Cov}(V,I)/I^3$. The covariance term shows why shared calibration information matters. A model must also account for effects such as instrumental drift when they materially influence the inference.^[ref]

A measurement principle is the phenomenon on which a measurement is based. The VIM gives the thermoelectric effect used to measure temperature as a physical example.^[ref] Naming that phenomenon identifies the physical connection that makes an observable signal informative about the measurand.

A thermocouple uses the Seebeck effect to produce an electric potential difference associated with temperatures along a circuit of dissimilar conductors. In a chosen approximation with constant coefficient $S$, the measurement model can be writtenwhere $T_r$ is the reference-junction temperature. For an original ideal example, choose $S=40\mu \mathrm{V}{\mathrm{K}}^{-1}$, $T_r=20{}^{\circ }\mathrm{C}$ and $V=2.00\mathrm{mV}$. The inferred temperature difference is $50\mathrm{K}$, so $T_h=70{}^{\circ }\mathrm{C}$. These stipulated values illustrate the model, not the calibration of a particular thermocouple type.

The reference condition matters physically: the same signal at $T_r=25{}^{\circ }\mathrm{C}$ would imply $T_h=75{}^{\circ }\mathrm{C}$ in this model. A readout that neglects the reference temperature can give a precise-looking indication without measuring the intended temperature correctly. The measurement principle explains why the signal arises; it does not by itself supply all corrections or the measurement uncertainty.

The measurement method describes how operations are organized around the principle, for example comparing an output with a reference. The measurement procedure specifies the actual conductors, junction arrangement, temperature references, readout settings, settling interval and calculations. Different measuring systems can share a measurement principle while implementing different methods and procedures.

Measurement reproducibility is measurement precision under specified reproducibility conditions of measurement. These include changed locations, operators and measuring systems; the measurement procedures may also differ. The comparison must state what changed and what remained common.^[ref] This is a metrological use of reproducibility, concerned with agreement of measured values under declared conditions.

An original comparison sends a stable reference block to three laboratories. Each laboratory uses its own differential length comparator and operator, measures the same specified measurand, and reports three length errors relative to the common reference, in micrometres:Each laboratory has sample standard deviation $1\mu \mathrm{m}$, yet their arithmetic means differ by as much as $6\mu \mathrm{m}$. Relative to the grand mean $2/3\mu \mathrm{m}$, the total squared deviation separates into within-laboratory and between-laboratory terms:Consequently the nine chosen values have pooled sample standard deviation $\sqrt{62/8}\approx 2.78\mu \mathrm{m}$. This is a descriptive statistic for these observations, not a universal estimator prescribed by the definition of measurement reproducibility. Reporting only the three within-laboratory spreads would conceal the disagreement.

The physical comparison must also monitor transport damage, reference stability and temperature corrections. A changing reference block could imitate a laboratory difference. Measurement reproducibility alone does not establish measurement trueness: all laboratories could share the same erroneous reference value or correction. The changed apparatus and conditions belong alongside the reported statistic.

A measurement result comprises the values attributed to a measurand together with the information needed to interpret those values. It commonly takes the form of a measured quantity value and its measurement uncertainty, but the result can contain a fuller probability distribution. A displayed number alone need not supply this information.^[ref]

For an original electrical resistance example, report a corrected value $R=100.020\Omega$ at $20{}^{\circ }\mathrm{C}$, with standard uncertainty $u(R)=0.010\Omega$. Specify that the uncertainty is a standard uncertainty, identify the resistor and the temperature condition, and retain the measurement procedure and measurement calibration records. Writing only “100.020” omits both the unit and the meaning of the last digits. Writing $100.020\pm 0.010\Omega$ without stating what the second number represents is also incomplete.

An alternative report might use expanded uncertainty $U=0.020\Omega$ with coverage factor $k=2$. This is a different uncertainty convention for the same central estimate; it is not a newly observed change in the resistor. A probability interpretation requires the distributional assumptions accompanying that convention.

An instrument's indication, a corrected measured quantity value and the complete measurement result therefore play different roles. Corrections from the measurement model, the specified measurand, environmental conditions and metrological traceability can all matter when comparing results. Extra displayed digits cannot replace the omitted measurement uncertainty.

Measurement uncertainty describes the spread of values reasonably attributable to a specified measurand, using the available information. It differs from measurement error, which is a difference from a reference value: the uncertainty can be quantified even when that difference is unknown. A useful result therefore states what was measured, the unit of measurement, the value, and how its uncertainty was evaluated.^[ref]

For a wire's electrical resistance obtained from $R=V/I$, uncertainty in the voltage and electric current propagates through that measurement model. Shared measurement calibration effects can make the inputs correlated, so adding percentages or treating every contribution as independent can be wrong. The measurement uncertainty budget records those assumptions. Report a standard uncertainty when a standard-deviation scale is intended, or an expanded uncertainty with its stated coverage factor; a bare plus-or-minus number does not specify which was used.

A measuring system combines one or more measurement instruments with the other devices needed to produce measured values for specified quantities over specified intervals. A system can also consist of a single instrument when it performs the required function on its own.^[ref] The system boundary identifies which physical components and signal-processing operations contribute to a measurement result.

For an original electrical example, assemble a current source, a test resistor, two connecting leads, a voltmeter and the calculation used to infer electrical resistance. The intended measurand is the test resistor's resistance $R_x$ at stated conditions. In a chosen ideal model, the current is $I$, the leads contribute total resistance $R_L$, and the readout has additive measurement zero offset $V_0$:Here the voltmeter measures an electric potential difference across the complete resistor-and-lead path, with negligible input-current loading.

With $I=1\mathrm{mA}$, $R_L=2\Omega$, $V_0=1\mathrm{mV}$ and indicated potential difference $103\mathrm{mV}$, the model gives $R_x=100\Omega$. Dividing the indication by the current without either correction would instead give $103\Omega$. The numerical resolution of the voltmeter alone therefore does not describe the whole measuring system's result.

A measuring chain describes successive signal transformations within such an arrangement. Its physical response and subsequent calculation belong in the system description, together with measurement calibration, reference conditions and relevant measurement uncertainty. Changing a lead connection, source setting or correction can change the result while leaving the main readout instrument unchanged. The measurement procedure records how that particular system is operated.

A metrologist studies measurements and the references, methods and uncertainties that make them comparable. The work connects a specified measurand to measurement calibration, measurement uncertainty and reproducible units of measurement. An experiment that improves the stability of a length standard or checks the response of a force probe is therefore investigating how confidently other experiments can compare their results.

Michael H. Ramsey is a researcher credited as chair of the working group and co-editor of the 2019 guide to measurement uncertainty arising from analytical sampling. The editorial page gives his affiliation as the University of Sussex and names fellow editors Stephen L. R. Ellison and Peter Rostron.^[ref]

The scientific problem is the difference between the collected material and the material about which a conclusion is wanted. For a measurand defined over a larger target, excellent measurement repeatability on one extract does not establish small analytical sampling uncertainty. The guide treats collection and analysis as stages of the measurement process.^[ref]

An original measurement model illustrates why replication at the right level matters:Here $\mu$ is the target mean, $S_i$ is the zero-mean deviation associated with independently collected sample $i$, and $A_{ij}$ is zero-mean analytical variation in repeat $j$. Suppose all these effects are mutually independent, with variances ${\sigma }_S^2$ and ${\sigma }_A^2$. Averaging $m$ analyses on each of $n$ collected samples givesFor stipulated variance components ${\sigma }_S^2=9$ and ${\sigma }_A^2=4$ in squared concentration units, three collections analysed twice give variance $9/3+4/6=11/3$, or standard uncertainty about $1.915$ concentration units. Six analyses of only one collection instead give variance $9+4/6=29/3$, or about $3.109$ units. Both plans use six analyses; only the first averages over independently collected material.

The calculation assumes independent collections and an appropriate model for the target; a shared systematic collection error is not removed by increasing $n$. It explains the measurement-design issue addressed by the guide without presenting these invented numbers as Ramsey's experimental observations.

A systematic error shifts results in a reproducible or correlated way relative to the measurand. Examples include zero offsets, gain miscalibration, nonlinearity, drift, geometrical misalignment, loading, background contamination and an inadequate response model. Repeated measurements under unchanged conditions can reduce independent noise without exposing such a shift; a small standard error is not proof of measurement accuracy.

Use traceable standards and independent measurement methods, test zero and span, reverse the physical signal where possible, and vary the conditions that could cause the bias. For common-offset cancellation by signal reversal, take measurements $y_{+}=S+b$ and $y_{-}=-S+b$. The half-difference estimates $S$ while the half-sum diagnoses an unchanged offset $b$. Drift between the two readings would violate that cancellation, so alternate or interleave measurements. Randomizing acquisition order separates slow drift from controlled parameter changes; blanks and calibration samples test backgrounds and instrument gain. Blinding helps reduce analysis choices that follow an expected answer.

Corrections have uncertainties and may be correlated across data. Include residual offsets and gains as nuisance parameters or propagate their calibration covariance, rather than treating a common calibration error as fresh independent noise for each datum. Document assumptions and check stability with controlled perturbations. These methods suppress or quantify specified systematic effects; they cannot guarantee elimination of every unknown bias.

Paul Brereton is a researcher in food safety, quality and authenticity. Queen's University Belfast identifies his interdisciplinary research on risks to the food system and his coordination of the TRACE and FOODINTEGRITY projects. The corresponding Fera project biography corroborates those scientific activities.^[ref]

Paul Brereton coauthored the collaborative trial in which multiple samplers collected wheat and green coffee from bulk lots. The design assessed variation arising in analytical sampling, rather than restricting replication to repeated laboratory readings.^[ref]

An original food-authenticity illustration distinguishes checking a product's composition from checking a label's claim. A mixture containing90 units of ingredient A and10 of B still totals100 units. A method measuring only total material would not reveal the substitution; a selective measurement for B or a validated compositional pattern could. Which observation answers the authenticity question depends on a defined measurand, representative analytical sampling and suitable comparison materials.

This is the scientific connection between laboratory method development and food-system investigation: an analytically accurate result must measure a property relevant to the claim being tested. A method's detection capability for one known substitution does not establish universal detection of every possible substitution.