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Instrument calibration applies measurement calibration to a particular measuring instrument: reference inputs with known measurement uncertainty are compared with its indications to obtain a relation for interpreting later readings. It determines a response and its measurement uncertainty; physically adjusting the instrument is a separate operation.^[ref]

For an original balance example, an empty pan indicates $0.020\mathrm{g}$ and a reference mass of $100.000\mathrm{g}$ indicates $100.220\mathrm{g}$. An assumed linear measurement model isA later indication $r=50.120\mathrm{g}$ givesThe correction here is $-0.120\mathrm{g}$; subtracting only the measurement zero offset leaves the scale error uncorrected. These illustrative readings are exact arithmetic inputs, not a claim of zero measurement uncertainty.

In a real comparison, apply several reference loads across the working range, remove and replace them, and repeat points while increasing and decreasing the load. Two points alone fit a line exactly and cannot establish linear response. Record environmental conditions, reference values and their measurement uncertainties, scatter and instrumental drift. Subsequent results inherit uncertainties in the indication and fitted parameters, including their covariance. Recheck after adjustment because the original response relation may have changed.

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.

James Taylor coauthored research on electronic-balance measurements of solid and liquid mass density, mass, and vessel volume. The study explained how an electromagnetic restoring force returns a loaded balance to a sensed null position, then related the readings to buoyancy and measurement calibration. It also treated pycnometer density measurement, where temperature-dependent vessel capacity must be distinguished from the balance reading.^[ref]

Joel B. Fowler is an electrical engineer whose work at NIST included the design and characterization of a water-bath blackbody source for radiometry. His 1995 paper describes a cavity whose temperature and effective emissivity could be controlled and measured accurately.^[ref]

The apparatus placed a shaped copper cavity in a stirred, temperature-controlled water bath. The cavity geometry increases the number of internal specular reflections, raising its effective emissivity toward one, while the bath reduces temperature variation. The paper reports operation from 278 K to 353 K and calculated emissivity 0.9997, increased to 0.99997 with a restricted aperture. Geometry, thermal control and measurement uncertainty accounting therefore work together to create a useful measurement calibration source.^[ref]

Identity and discipline: upload.wikimedia.org/wikipedia/commons/7/74/A_Third_Generation_Water_Bath_Based_Blackbody_Source_(IA_jresv100n5p591).pdf

Klaus Danzer is an analytical chemistry researcher who prepared the 1998 IUPAC recommendations on single-component measurement calibration with Lloyd A. Currie. The paper identifies an affiliation at Friedrich Schiller University of Jena, and the NIST publication record gives both authors' full names. The recommendations organize how measured signal is related to the amount or concentration of an analyte under specified conditions.^[ref]

An analytical calibration function separates the response model from its use to infer an unknown. For an original linear example, let concentration $c$ be measured in $mg/L$ and signal $y$ in arbitrary instrument units. Suppose known standards at $c=0,1,2,4$ give $y=2,7,12,22$, exactly satisfyingAn unknown giving $y=17$ has inferred concentration $c=(17-2)/5=3mg/L$. Ignoring the intercept instead gives $17/5=3.4mg/L$, an error of about thirteen percent. A nonzero blank response is not automatically analyte in the unknown.

The same example shows why a calibration curve is more than a unit conversion. A small perturbation of the inverse relation givesReadout variation, uncertainty in the intercept and uncertainty in the slope affect the inferred concentration in different ways. Their covariances can matter when the coefficients are fitted together. These are independently derived sensitivities for the stipulated model, rather than experimental values attributed to Danzer's paper.

A practical calibration measures standards and unknowns with compatible preparation, sample matrix and instrument settings. Repeated standards can reveal measurement noise and time-dependent response; a model established up to $4mg/L$ does not by itself validate its extension to $8mg/L$. The recommendation's explicit dependence on specified conditions makes those experimental choices part of the meaning of calibration, not incidental details.^[ref]

Danzer also coauthored the 2002 distinction between extraction recovery and apparent recovery. That distinction prevents agreement after calibration from being mistaken for proof that no analyte was lost in sample preparation.^[ref]

Choose an original simulated model: a primitive cubic lattice with one identical point scatterer per primitive unit cell, edge $a=0.400\mathrm{nm}$, and incident X-ray wavelength $\lambda =0.200\mathrm{nm}$. These are stipulated lengths, not measurements of sodium chloride. Suppose orientation information has already assigned the two observed reflections the Miller indices $(100)$ and $(110)$. The detector deflection is $2\theta$, where $\theta$ is the Bragg angle.

Use the existing interplanar spacing formula and Bragg's law, with higher diffraction orders absorbed into the reflection indices:The simulated $(100)$ reflection has $\sin \theta =1/4$, hence $2\theta =28.9550^{\circ }$. The $(110)$ reflection has $\sin \theta =\sqrt{2}/4$, hence $2\theta =41.4096^{\circ }$. Their squared sines are $0.0625$ and $0.125$, in ratio $1:2$. Substituting the unrounded angles independently givesAgreement tests a proposed indexing and scale together; disagreement would prompt checks of the indexing, wavelength, angular zero, or the assumed unit cell geometry.

Two peak positions alone do not establish the indexing. Assigning the same two angles to $(110)$ and $(200)$ would give $N=2,4$ and $a=0.400\sqrt{2}\simeq 0.565685\mathrm{nm}$ instead. More reflections, orientation information and systematic absences help discriminate such assignments. An unobserved reflection can be inaccessible, weak or extinguished by the atomic basis; absence is not automatically evidence for a larger interplanar spacing.

Absolute scale also requires measurement calibration of the wavelength. To first order, differentiation of the inferred length giveswith angular changes in radians. A stipulated detector-zero error $\delta (2\theta )=+0.100^{\circ }$, with correct wavelength, biases the first inferred edge downward by approximately $0.338\%$ and the second by $0.231\%$. A common angular offset therefore need not cancel when two inferred lengths are compared. These are model sensitivity estimates, not historical instrument uncertainties.

L. L. Lucas authored a 2000 comparison of water standards containing tritium, using liquid scintillation counting to measure their massic activity ratios. Massic activity is radioactive activity divided by sample mass. The work compared these measured ratios with ratios obtained from recorded mass-based dilutions, finding agreement within the stated measurement uncertainties.^[ref]

The comparison illustrates an important measurement calibration principle: independently obtained ratios can test consistency without making every source of error disappear. Agreement between counting and dilution supports the relationship between the standards. It does not imply zero measurement uncertainty, nor does it by itself establish an absolute radioactive activity scale. The study's contribution was a quantitative connection between reference samples, rather than simply a count from one detector.^[ref]

Connect a passive electrical load of electrical resistance $R_L>0$ from the junction to the reference. In the steady direct current model, $R_L$ and $R_2$ share the same electric potential difference and are connected in parallel. Their equivalent electrical resistance isThis is the replacement required when a voltmeter is connected across $R_2$. The inspected teaching guide explicitly models the meter this way.^[ref]

The Kirchhoff's current law proof makes the changed assumption visible:The upper resistor carries the sum of the lower and load electric currents, so the two original resistors no longer have equal electric currents. In the stipulated $R_L=10\mathrm{k\Omega }$ example, $R_p=5\mathrm{k\Omega }$, the upper current is $2/3\mathrm{mA}$, and each lower branch carries $1/3\mathrm{mA}$.

For a digital multimeter used as a voltmeter, let its input electrical resistance on the selected voltage range be $R_m$. With no other load, set $R_L=R_m$; if an intended load is also present, use $R_L\parallel R_m$ in place of $R_L$. Two instruments connected simultaneously add two branches. Their ideal indicated value equals the voltage of this altered circuit; voltmeter loading can exist even with perfect measurement calibration. Real readout additionally has measurement uncertainty, offsets, and finite resolution. Consult the actual range and input specifications rather than assigning every digital multimeter the same $R_m$.

For an oscilloscope, the resistance-only model applies to settled direct current with a compatible input coupling setting. The oscilloscope input and cable also have capacitance; changing signals can therefore have loading that depends on frequency. A probe changes the impedance seen at its tip and the relation between tip voltage and displayed voltage; use the probe-and-input combination's specification and attenuation setting. NJIT's guide models the parallel electrical resistance and capacitance and adjusts probe compensation with a square-wave readout.^[ref]

The ideal measurement calibration is a straight line through the equal-pressure zero. For the stipulated water mass density and gravitational acceleration above, its slope is $9.81\mathrm{Pa}$ per vertical millimetre. A lower liquid mass density gives a larger length change for the same pressure, improving the height-reading contribution to measurement resolution but requiring more vertical space for a given pressure range. The pressure scale depends on the actual mass density, which depends on temperature; $1000\mathrm{kg}{\mathrm{m}}^{-3}$ here is a chosen model value, not an exact property of all water samples.

Suppose each liquid meniscus height is rounded to the nearest scale division $q=1\mathrm{mm}$. Each height measurement error lies within $q/2$, so the error in their difference lies within $q$. The resulting pressure error is bounded by $\rho gq=9.81\mathrm{Pa}$. To quantify a standard uncertainty, make the additional model assumption that the two rounding errors are independent random variables, each having a continuous uniform distribution on $[-q/2,q/2]$. Each then has variance $q^2/12$; adding the two variances givesHere $u$ denotes standard uncertainty. The $9.81\mathrm{Pa}$ worst-case bound and $4.0049\mathrm{Pa}$ standard uncertainty answer different questions. At $588.6\mathrm{Pa}$ they are respectively $1.67\%$ and $0.680\%$ of the reading. Shared reading bias or a different reading method can violate the assumed independence.

If the mass density, gravitational acceleration and height difference also have small independent standard uncertainties, a first-order estimate follows from the partial derivatives of $\Delta p=\rho g\Delta h$:This displays why a finer scale does not remove uncertainty in mass density or gravitational acceleration. A pressure transducer compared against the column needs its own differential-pressure calibration to establish an electrical response.

M. D. Morris, or Michael D. Morris, is a chemist whose research connects Raman spectroscopy and chemical imaging with the composition and mechanics of bone and other tissues. His University of Michigan profile describes both instrument development and image processing for spectroscopic contrast.^[ref]

A central idea is to retain a Raman spectrum at each sampled position instead of recording only brightness. A map of a phosphate band can then emphasize mineral, while a different spectral contribution carries information about protein. Morris and W. F. Finney also studied how bone spectra respond during controlled elastic loading.^[ref]

As an original two-channel example, suppose reference responses for mineral and an organic component give measured intensities $I_1=2a+b$ and $I_2=a+3b$. Measurements $(I_1,I_2)=(5,5)$ yield $a=2$ and $b=1$ by solving the linear system. These are model response coefficients, not automatically mass fractions: quantitative composition additionally requires suitable reference specimens and measurement calibration. The example shows why spectral information can separate contributions that a single brightness image mixes.

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 calibration establishes a relation between an instrument's indications and reference values, with the relevant measurement uncertainty. It is distinct from changing the instrument until it reads correctly: an instrument can be calibrated by determining a response curve and applying a correction afterward.

A practical voltage experiment connects a meter to several reference values across its working range and repeats the sequence in both directions. Fitting $V_{\mathrm{indicated}}=a{V}_{\mathrm{reference}}+b$ estimates a scale factor and measurement zero offset; departures from that relation test the response model. Repetition at later times checks instrumental drift. Uncertainty in the reference, the fitted parameters, and unresolved departures belongs in the measurement uncertainty budget, even if the displayed readings appear very stable.^[ref]

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 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 trueness describes how closely the mean of indefinitely many replicate measured values would agree with a reference value. It concerns systematic error, rather than the random spread described by measurement precision. The VIM treats measurement trueness as qualitative: a numerical error or bias estimate is not itself a numerical value of trueness.^[ref]

An original measurement model separates the two issues:Here $b$ is a fixed offset from the stipulated reference and $\epsilon$ is random measurement error. Thus $\mathbb{E}[X]-x_{\mathrm{ref}}=b$, while the standard deviation of individual readings is $\sigma$. For $n$ independent random variables following this same model, averaging givesMore readings reduce the random scatter of the mean without removing $b$.

For a stipulated length example, instrument A has $b=0.40\mathrm{mm}$ and $\sigma =0.10\mathrm{mm}$; instrument B has $b=0$ and $\sigma =0.50\mathrm{mm}$. A is more precise, while B's limiting mean agrees better with the reference. With 25 independent readings each, the means have standard deviations $0.02\mathrm{mm}$ and $0.10\mathrm{mm}$ respectively, and their expected offsets remain $0.40\mathrm{mm}$ and zero. These are chosen model parameters, not experimental determinations.

Experimentally, compare repeated readings of a stable measurement standard with its assigned value under specified conditions. A finite observed mean difference estimates an offset; its interpretation includes sampling variation and the reference's measurement uncertainty. Determine an appropriate correction through measurement calibration, apply it in the measurement model, and check it with further reference measurements. Neither a small sample spread nor a single reading equal to the reference establishes measurement trueness.

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 chain is one successive signal path through a measuring system, beginning at a sensor and ending at an output element. The VIM illustrates this with both an electro-acoustic chain and a mechanical chain; a measuring chain need not be electronic.^[ref]

Consider an original force measurement: a load cell produces an electric potential difference, an amplifier scales that signal, an analog-to-digital converter encodes it, and software converts the code to a force value. This is one measuring chain within the larger measuring system, which also includes its excitation supply, references and mounting arrangements. The load cell acts as a measuring transducer.

In an ideal linear model, let sensor response be $S=2\mathrm{mV}{\mathrm{N}}^{-1}$, amplifier gain $G=100$, and converter step $q=1\mathrm{mV}$ per code increment. If the additive output offset is $n_0=10$ increments, the numerical output isThus $n=610$ implies $F=(610-10)/200=3\mathrm{N}$. These are stipulated teaching values, not measured equipment performance.

The example exposes why measurement calibration must describe the relevant path. Changing amplifier gain changes the relation between output and force even if the load cell is untouched. A saturated stage can also discard information that later conversion cannot restore. Gain uncertainty, offsets, instrumental drift and finite conversion steps contribute at different points; treating the final display's increment as the whole measurement uncertainty overlooks those contributions.

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.