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The dynamic range of a measurement procedure is the ratio between the largest and smallest positive inputs it can usefully distinguish under specified conditions. The quantity, bandwidth, observation interval, measurement calibration and acceptance criteria must accompany the number. An upper boundary might be set by amplifier clipping, while a lower boundary might require a specified signal-to-noise ratio. More displayed digits do not by themselves establish either boundary or better measurement precision.

Consider a photodiode responsivity measurement in which a photodiode produces electric current $i=R_{\lambda }P$ from optical power $P$, with spectral responsivity $R_{\lambda }$ in amperes per watt. A transimpedance amplifier with transimpedance $R_T$ produces an output electric potential difference $v=R_{T}i=KP$, where $K=R_T{R}_{\lambda }$ is expressed in volts per watt. If a chosen detection criterion is $k$ times the root mean square output noise ${\sigma }_v$, and the linear output ceiling is $v_{\max }$, the noise-limited dynamic range isIncreasing $K$ shifts this input interval toward smaller optical power; it does not enlarge its ratio when the stipulated output noise and output ceiling remain fixed. The corresponding input-referred noise is ${\sigma }_v/K$.

An original numerical example makes the distinction concrete. Stipulate ${\sigma }_v=0.5\mathrm{mV}$ in either setting, $k=5$, $v_{\max }=1\mathrm{V}$, and no other limiting effect. A high-sensitivity setting $K_H=10^{5}V/W$ covers $25\mathrm{nW}$ to $10\mu \mathrm{W}$; a lower-sensitivity setting $K_L=10^{3}V/W$ covers $2.5\mu \mathrm{W}$ to $1\mathrm{mW}$. Each interval has $D=400$. With compatible measurement calibration and range-overlap calibration in their common interval, gain ranging covers a contiguous union from $25\mathrm{nW}$ to $1\mathrm{mW}$: $D=40000$. These are optical power ratios, so their values in decibels are $10{\log }_{10}D$, approximately $26.02\mathrm{dB}$ and $46.02\mathrm{dB}$ respectively.

The equal-noise assumption isolates the geometric effect of gain ranging; it is not a specification for an actual instrument. A real transimpedance amplifier changes several properties together: its feedback network affects bandwidth, noise and allowable input electric current. The DLPCA-200 manufacturer sheet shows a current-to-voltage converter followed by programmable amplification and gives separate tables for these dependencies.^[ref] To test a combined dynamic range, vary a stable light source through the overlap, compare the two estimates of optical power, and allow the output to settle after switching. Agreement within the required measurement uncertainty is evidence that the two ranges can be joined; the mere presence of two gain switches is insufficient.

An interlaboratory comparison organizes measurements of the same or similar items by at least two laboratories under agreed conditions. Its purpose may be to compare measurement reproducibility, evaluate a measurement procedure, or assess participants through proficiency testing. The last purpose evaluates performance against criteria specified beforehand; it is one use of interlaboratory comparison, not a synonym for every such study.^[ref]

A comparison begins with its experimental design. When the aim is to compare laboratory analysis, the organizer needs evidence of reference material homogeneity and reference material stability: unequal or changing test items would otherwise be confounded with differences between laboratories. If collection is part of the measurement procedure, the design must instead include analytical sampling explicitly. In a 2002 collaborative study, several samplers collected wheat and green coffee from bulk lots. The reported between-sampler variation was significant for two wheat analytes; for coffee, a significant repeatability contribution was detected without a significant sampling-reproducibility contribution. Thus even the importance of collection differences depended on the material and analyte.^[ref]

For a common proficiency-test z-score, let laboratory $i$ report $x_i$, let the assigned value in proficiency testing be $x_{\mathrm{pt}}$, and let the standard deviation for proficiency assessment be ${\sigma }_{\mathrm{pt}}>0$. ThenThe denominator describes the scheme's assessment scale. It is not automatically the laboratory's own measurement uncertainty or the observed standard deviation of participants. The guide distinguishes this proficiency-test z-score from scores incorporating uncertainty, and discusses choosing the assessment scale to match the intended use.^[ref]

An original hypothetical example shows why the assigned value matters. Suppose three laboratories report $9.8$, $10.0$ and $10.2$ in the same units, with a stipulated assessment scale ${\sigma }_{\mathrm{pt}}=0.2$. Using their arithmetic mean as an interlaboratory consensus value gives $x_{\mathrm{pt}}=10.0$ and scores $(-1,0,1)$. An independently established assigned value of $11.0$, with the same assessment scale, instead gives $(-6,-5,-4)$. Close agreement between participants is compatible with a shared displacement from an independent reference.

The identifiability issue is visible in a simple model,where $\theta$ is the reference quantity value, $b$ is a common systematic error, and ${\epsilon }_i$ describes laboratory-specific deviations. Pairwise differences eliminate both $\theta$ and $b$. Moreover, replacing $(\theta ,b)$ by $(\theta +c,b-c)$ leaves every predicted $x_i$ unchanged. Consequently agreement alone cannot identify a common bias. Evidence from an independently characterized certified reference material or an independent measurement calibration can help resolve this ambiguity. Its own measurement uncertainty and any shared dependencies must then enter the comparison model; one should not silently interpret the simple proficiency-test z-score as a full uncertainty calculation. This is the practical importance of distinguishing measurement precision, measurement trueness and shared bias in an interlaboratory consensus.

Intermediate measurement precision is measurement precision under declared intermediate precision conditions of measurement. The measurement procedure and location remain the same, measurements extend over a longer period, and specified changes can include operators, measuring systems or measurement calibration. It lies between a tightly restricted measurement repeatability study and a comparison across locations for measurement reproducibility.^[ref]

For a concrete study, one laboratory could measure the same stable reference block each working day with a differential length comparator, taking four complete placements per day. Keep the procedure fixed, record temperature and operator, and retain the day labels. The labels let the study distinguish variation between days from rapid reading-to-reading variation.

An original measurement model makes that distinction explicit:Here $D_d$ is a shared daily offset, while ${\epsilon }_{dj}$ varies between placements. Assume zero-mean independent random variables, independent also between days. A single measurement then has standard deviation $\sqrt{{\tau }^2+{\sigma }^2}$. For stipulated $\tau =0.30\mu \mathrm{m}$ and $\sigma =0.10\mu \mathrm{m}$, this is $0.316\mu \mathrm{m}$, even though within-day scatter reflects only $0.10\mu \mathrm{m}$.

For four readings from one day, the daily arithmetic mean hasAveraging four such means from independent days reduces that modelled standard deviation to $0.152\mu \mathrm{m}$. Four same-day readings do not average away their shared daily offset. The numbers illustrate the model, not measured comparator performance. A real study must estimate its components from enough days and investigate trends: deterministic instrumental drift is not automatically an independent daily offset.

An intermediate precision condition of measurement is part of the specified conditions used to assess intermediate measurement precision. The measurement procedure and location remain common, while replicate measurements extend over a longer period. Declared changes can include operators, measuring systems, reference devices used for measurement calibration, or new calibrations.^[ref] The record should state what changed as well as what was held fixed.

For an original length study, use the same laboratory, reference block and written comparator procedure in two sessions three weeks apart. Two operators each perform the complete remove-and-replace measurement cycle in both sessions. Record instrument identity, measurement calibration, temperature and settling conditions rather than treating “same laboratory” as a complete description.

Consider the following stipulated session means in millimetres:The operator difference is $6\mu \mathrm{m}$ within either session, and the session difference is $10\mu \mathrm{m}$ for either operator. In an additive model, those two comparisons distinguish the respective contributions because both operators appear in both sessions.

If operator A had measured only in session 1 and operator B only in session 2, the observed $16\mu \mathrm{m}$ difference would combine the two changes. It could not be uniquely attributed to operator or elapsed time from that design alone. Repeated complete trials within each operator-session combination are still needed to assess random scatter; four chosen means do not establish a general precision value.

A measurement repeatability study keeps more conditions fixed over a short interval. Changing locations and measuring systems leads toward reproducibility conditions of measurement. The intermediate specification identifies the particular longer-term changes included in the precision study, rather than promising that every source of variation has been sampled.

Differences between supplied proficiency-test items can contribute to a proficiency-test z-score even when the laboratory's own measurement procedure has zero systematic error. Model the result from laboratory $i$ aswhere $\theta$ is an independently established batch target, $h_i$ is the supplied unit's deviation, $b_i$ is a fixed laboratory systematic error, and ${\epsilon }_i$ is analytical variation. Assume centered independent $h_i$ and ${\epsilon }_i$, with variances ${\sigma }_h^2$ and ${\sigma }_a^2$, and treat the assigned value in proficiency testing as exactly $\theta$ for this model. Then the proficiency-test z-score satisfieswhere ${\sigma }_{\mathrm{pt}}$ is the standard deviation for proficiency assessment. A large deviation cannot be attributed to $b_i$ from the score alone. The primary guide requires item reference material homogeneity and reference material stability to remain appropriate when a stricter assessment scale is selected.^[ref]

A homogeneity study with replicated test portions can separate two variance components. Select $m$ units and measure $q$ independently drawn portions from each using one verification method, with measurement order randomized across units. Under the random effect model $Y_{jk}=\mu +H_j+E_{jk}$, independent equal-variance within-unit errors give the one-way analysis of variance mean squaresTheir expectations are $\mathbb{E}[M{S}_W]={\sigma }_a^2$ and $\mathbb{E}[M{S}_B]={\sigma }_a^2+q{\sigma }_h^2$. A method-of-moments variance component estimate is therefore $(M{S}_B-M{S}_W)/q$. Here the within-unit component is treated as analytical variation; variation between portions of the same unit would also enter it unless separated by further measurements. Clipping a negative estimate to zero enforces a nonnegative variance component; it introduces boundary bias and does not establish perfect material uniformity.

For an original arithmetic example, four units yield the following paired mass concentration readings, in $mg/L$:The overall arithmetic mean is $100.05$. These chosen data give $M{S}_W=0.02$, $M{S}_B=0.50$ and ${\hat{\sigma }}_h^2=0.24$, with squared concentration units. Suppose a participant also has analytical variance $0.02$ and the assessment scale is $0.20mg/L$. The stipulated unbiased score then has standard deviation $\sqrt{0.26}/0.20\approx 2.55$, despite the absence of a laboratory systematic error in the model.

Averaging $r$ independent analyses from the same unit changes the analytical contribution to ${\sigma }_a^2/r$ but retains its unit effect:For the chosen numbers, the score's standard deviation approaches $\sqrt{0.24}/0.20\approx 2.45$ as $r$ increases. Repeatedly reading one aliquot may reduce an even narrower instrumental contribution. Sampling independently supplied units addresses a different source of variation.

This is an explanatory measurement model, not a prescribed proficiency-testing acceptance rule. Its variance components require evidence about portion size, preparation and measurement conditions. Instrumental drift confounded with unit order, changing material, correlated errors or an uncertain consensus benchmark require additional terms and an appropriate design. The provider's homogeneity evidence is essential to interpreting the score; a smaller numerical scoring denominator does not improve the items.

A measurement method describes the general logical arrangement of operations used to make a measurement. A measurement procedure supplies the detailed instructions for implementing that arrangement with a particular measuring system. The VIM lists substitution measurement methods, null measurement methods, differential methods, and direct or indirect methods among the possible descriptions.^[ref]

For an original substitution measurement method, drive an unknown resistor with a fixed electric current $I$ and record a voltmeter indication. Replace it by an adjustable reference resistor without changing $I$ or the other relevant conditions, and adjust the reference until the indication agrees. In an ideal measurement model,so matching $v_x=v_s$ implies $R_x=R_s$. The common additive readout offset $v_0$ cancels. This cancellation depends on the current, connections and offset remaining common; the name of the method alone does not guarantee those conditions.

A null measurement method instead adjusts a reference to make a difference signal vanish, as in a balanced Wheatstone bridge. A resistance inferred from separately measured electric potential difference and electric current uses the relation $R=V/I$. These are descriptions of how the information is obtained; the operational record must still specify apparatus, settings, timing, reference values and the measurement uncertainty. The measurement principle concerns the physical phenomenon used, while the measurement method concerns the organization of the measurement. Different methods can exploit the same physical principle.

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.

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 noise-limited detection threshold is the decision boundary above which a measured statistic is declared to contain a signal, with the boundary chosen from a specified noise model and decision criterion. It belongs to a measurement procedure: filtering, observation time, baseline correction and the definition of a positive detection affect the result. A threshold in observed data is distinct from the true signal level required for a specified detection probability.

For an original one-sided Gaussian model, let $Y=A+N$, where $A\ge 0$ is the signal and $N\sim \mathcal{N}(0,{\sigma }^2)$. Declare a detection when $Y>T$ and choose $T=k\sigma$. If $\Phi$ is the cumulative distribution function of the standard normal distribution, thenHere $\alpha$ is the per-observation false alarm rate. The model assumes the same additive noise distribution with and without the signal.

With $k=5$, the noise-only crossing probability is approximately $2.87\times 10^{-7}$ for one independent observation. Yet a signal of size $A=5\sigma$ is detected only half the time: it puts the distribution's center exactly at the decision boundary. “Five times noise” therefore does not by itself mean reliable detection of a signal that large.

To require both a chosen false-positive probability $\alpha$ and a missed-detection probability $\beta$, this model givesFor $\alpha =\beta =0.05$, the two values are approximately $1.645\sigma$ and $3.290\sigma$. This distinction accords with the VIM definition of a limit of detection, which specifies both kinds of decision error rather than equating the limit with the observed threshold.^[ref]

The experiment should measure baseline fluctuations under the same bandwidth, loading and observation rule used for signal detection, then test known signal levels. Repeated searching also matters: for $M$ independent noise-only observations, the chance of at least one threshold crossing is $1-(1-\alpha {)}^M$. Correlated samples require a different calculation. Baseline drift, non-Gaussian fluctuations and signal-dependent noise likewise invalidate a threshold inferred from the simple model without further characterization.

Polarity reversal measurement separates a signal that changes sign on reversal from an additive contribution that does not. If readings in two polarities obeythen the half-difference estimates the signal, $\hat{s}=(y_{+}-y_{-})/2$, while the half-sum estimates the unchanged offset, $\hat{b}=(y_{+}+y_{-})/2$. The procedure depends on reversing the intended signal while preserving the relevant offset.

A primary electrical application is cancellation of thermoelectric offset in low-level measurements. The Keithley handbook describes reversing sources when comparing small electric potential differences, and reversing test current for low-electrical resistance measurements. A thermal offset can arise at dissimilar-metal junctions and remain approximately unchanged during a sufficiently short cycle.^[ref]

For an original resistance example, a bipolar current source applies $+I$ and $-I$ to a resistor while a voltmeter measuresLet $I=10\mathrm{mA}$ and readings be $103$ and $-97\mu \mathrm{V}$. Their half-difference is $100\mu \mathrm{V}$, giving $R=10\mathrm{m\Omega }$; their half-sum reveals a $3\mu \mathrm{V}$ offset. Using only the positive reading would instead give $10.3\mathrm{m\Omega }$.

An offset changing from $b_{+}$ to $b_{-}$ leaves resistance error $(b_{+}-b_{-})/(2I)$. A $2\mu \mathrm{V}$ change in this example therefore leaves $0.1\mathrm{m\Omega }$ error. Allow electrical settling after reversal while keeping the cycle short relative to material temperature changes. The handbook explicitly notes this time-scale requirement.^[ref] Reversal does not automatically remove lead resistance, current-magnitude errors, self-heating or every offset in the circuit. Record the actual reversal operation and timing with the measurement procedure.

Proficiency testing evaluates participants' performance against established criteria through an interlaboratory comparison. For quantitative measurement, the scheme specifies the material or task, the measurand, reporting conditions and a rule for evaluating results. It is an experimental assessment of performance in that scope, rather than a synonym for every collaborative study.^[ref]

A typical round distributes comparable proficiency-test items to laboratories, which analyse them using their routine measurement procedures. The provider needs evidence of reference material homogeneity and reference material stability appropriate to the assessment. Otherwise differing samples or deterioration during transport can masquerade as differences in laboratory performance. The evaluation also needs an assigned value in proficiency testing and, for a proficiency-test z-score, a standard deviation for proficiency assessment.^[ref]

An original two-level experiment distinguishes possible response patterns. Suppose independent assigned values are $10$ and $20$ in the same units. One laboratory reports $11$ and $22$; another reports $11$ and $21$. The first pattern fits a multiplicative factor of 1.10 at both levels, whereas the second fits an additive offset of 1. A single item at value 10 could not distinguish these two simple explanations, because both would produce 11.

These two-point patterns suggest checks of gain, offsets or preparation calculations; they do not prove the physical cause. Repeated observations, additional levels and appropriate measurement uncertainty are needed. If the scheme uses assessment scale 0.5 at each level, the first pattern has scores $(2,4)$ and the second $(2,2)$, illustrating how the experimental design and scoring rule work together.

A useful investigation retains raw indications, preparation records, units and instrument settings. Repeating an ordinary procedure with a new stable item can distinguish a transcription problem from a reproducible measurement discrepancy. Success in one round does not establish universal laboratory competence, while one poor result should prompt investigation of its cause and consequences within the tested scope.^[ref]

A proficiency-test item is the material, artefact, instrument or information supplied for assessment in a proficiency testing scheme. It is the object to which a specified task and result refer; it is not itself the participant's reported result or the assigned value in proficiency testing. The primary guide's definition covers samples and physical standards as well as data or other information used for assessment.^[ref]

For a material example, the provider can prepare a batch, divide it into sealed units, and distribute one coded unit to each laboratory. The specified measurand, amount to be sampled, preparation instructions, reporting units and handling conditions belong to the comparison design. The material must support the particular measurement procedures being assessed, rather than merely have a convenient nominal concentration.

Reference material homogeneity and reference material stability protect the interpretation. An original counterexample gives two laboratories bottles whose actual mass concentrations are $8$ and $12mg/L$, while both are scored against $10mg/L$ with assessment scale $0.5mg/L$. Perfect measurement of each bottle produces proficiency-test z-scores of $-4$ and $+4$. Those discrepancies arise from unequal items, not from incorrect laboratory measurement. A perfectly stable material can still be nonuniform, and a uniform batch can still change during transport.

A simple item heterogeneity contribution to proficiency-test scores can be written in a measurement model,where ${\delta }_{j(i)}$ describes the supplied unit's deviation, $b_i$ the laboratory effect and ${\epsilon }_i$ residual variation. Observing $x_i$ alone cannot assign all of its difference from $\theta$ to $b_i$. Replicate portions from selected units and stability checks across the relevant interval supply additional evidence.

The required degree of item uniformity depends on the assessment criterion. Narrowing the standard deviation for proficiency assessment can make previously tolerable item differences significant. The guide consequently requires checking whether item homogeneity and stability remain appropriate when choosing a stricter assessment scale.^[ref]

When a single artefact is circulated sequentially, changes between visits can similarly be confounded with laboratory differences. Record the item identifier, its preparation or transport history and the actual observation conditions so the reported value refers to a reproducible task.

A repeatability condition of measurement belongs to a specified set of conditions used to assess measurement repeatability. The VIM set includes a common measurement procedure, operator, measuring system, operating conditions and location, with repeated measurements of the same or similar objects over a short period.^[ref] A condition is therefore meaningful relative to the whole declared set, rather than being a property supplied by the word repeat alone.

For a physical length experiment, specify one operator, one differential length comparator, a reference block, the contact orientation, the remove-and-replace cycle, the settling rule and a short session after thermal equilibration. Keep the measurement calibration fixed during that series. Record every completed measurement, including the elapsed time and block temperature. Reading the unchanged display repeatedly and replacing the block between readings are different measurement procedures, even if the displayed numbers happen to coincide.

An original thermal calculation explains why the operating conditions matter. Suppose a metal reference block has length $L_0=100\mathrm{mm}$ at $T_0$, with stipulated constant linear thermal expansion coefficient $\alpha =12\times 10^{-6}{\mathrm{K}}^{-1}$. In the small-expansion model,A temperature rise of $2\mathrm{K}$ changes its length by $0.0024\mathrm{mm}=2.4\mu \mathrm{m}$. If the intended measurand is length at $T_0$, that change requires an appropriate correction; it should not be silently interpreted as random comparator scatter. This calculation holds other thermal effects fixed and does not model expansion of the comparator itself.

The same room and a short elapsed time do not prove that the relevant conditions were stable. Record observed deviations and assess their effect. Deliberately varying operators over several days at the same location leads to a study of intermediate measurement precision; varying locations and measuring systems leads toward measurement reproducibility.

A reproducibility condition of measurement belongs to a declared set of conditions used to assess measurement reproducibility. The VIM set includes different locations, operators and measuring systems, with replicate measurements on the same or similar objects. The systems may also use different measurement procedures; the specification should identify both changed and unchanged conditions.^[ref]

For an original physical comparison, laboratories A and B measure the same stable reference block's length at a stated reference temperature. Each laboratory uses its own operator and differential length comparator. Record the block identity, temperature observations, applied corrections, procedure version and instrument reference in each result. Measurements before and after transport help distinguish reference changes from laboratory differences.

Suppose the stipulated readings, in millimetres, areThe laboratory means are $50.001$ and $50.011\mathrm{mm}$, differing by $10\mu \mathrm{m}$. Each pair has sample standard deviation $\sqrt{2}\mu \mathrm{m}$. Small within-laboratory scatter therefore does not remove the difference between laboratory means.

Because location, operator and system changed together, this comparison alone cannot identify which caused the difference. In a chosen additive measurement model,a $10\mu \mathrm{m}$ difference could be assigned entirely to a system contribution or entirely to a location contribution, with the same predicted means. Separating those explanations needs additional comparisons that vary their combinations.

The conditions describe the experiment's scope; they are not a numerical precision result by themselves. Report the complete measurand and what remained common, including any shared measurement standard. A common reference error can affect both laboratories, so agreement under changed conditions does not by itself establish measurement trueness.

A substitution measurement method compares an unknown with a reference by placing them successively in the same relevant position of a measuring system. The comparison seeks a match or measures a difference while retaining as much of the system response as possible. The VIM lists substitution among measurement methods; the detailed sequence belongs to the measurement procedure.^[ref]

For a physical application, NIST's double substitution weighing procedure compares a standard and an unknown of the same nominal mass twice, using a weighing instrument as a comparator. It also determines response with a known sensitivity mass and includes measurement-assurance checks.^[ref] The measurement standard supplies the reference value; the comparator need not establish the whole mass independently from its own zero.

An original simplified drift model shows why sequence matters. Let a comparator indication at equally spaced time index $t$ bewith fixed response coefficient $a$, additive offset $b$ and linear instrumental drift $d$. Observe standard S at $t=0$, unknown X at $t=1,2$, and S again at $t=3$. The mean X indication and mean S indication have the same mean time $3/2$, givingBoth the constant offset and the stipulated linear drift cancel. For $m_S=m_0=100\mathrm{g}$, $a=500{\mathrm{g}}^{-1}$, $b=10$ and $d=2$, an unknown $m_X=100.004\mathrm{g}$ gives indications $10,14,16,16$. Their paired means differ by two, recovering $0.004\mathrm{g}$.

This teaching sequence isolates one cancellation mechanism; it is not the full NIST procedure. Nonlinear drift, unequal timing, changing response or different positioning can defeat it. NIST's procedure separately addresses buoyancy and uncertainty contributions.^[ref] A close match does not remove the reference's measurement uncertainty or establish that the substitution left every relevant condition unchanged.