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A record of uncertainty contributions in a specified measurement model, their evaluation methods, sensitivity coefficients and relevant correlations. For $y=g(x_1,\dots ,x_n)$, first-order propagation of uncertainty gives $u_y^2={\sum }_{ij}({\partial }_{i}g)({\partial }_{j}g)\mathrm{Cov}(x_i,x_j)$. A correction for a known systematic error can itself have uncertainty; applying the correction does not remove that contribution.^[ref]

Measure an unwanted effect separately or design a comparison in which it cancels. Calibration against reference standards at several settings tests zero offset, gain and nonlinearity. Blanks reveal background. Reversal is useful when the wanted response is odd and the nuisance is even: $D_{\pm }=\pm S+b$ gives $S=(D_{+}-D_{-})/2$ and $b=(D_{+}+D_{-})/2$. For example reversing current in a resistance measurement cancels a stable thermoelectric voltage, but does not cancel the current-scale error or even-in-current Joule heating.

Interleave reversals to detect drift; interchange sensors or leads to expose asymmetry. Null or bridge methods reduce dependence on detector gain. Alignment checks, shielding and temperature control target specified physical couplings. Independent measurement methods provide a stronger test than repetition of the same setup. Repeated readings reduce independent random errors, not a shared bias. Correct significant identified effects and propagate the uncertainty of the correction and standards; complete removal of an unknown systematic error cannot generally be certified.^[ref]

Identify the physical coupling that causes a systematic error and change the apparatus so that it can be measured or cancelled. measurement calibration against known reference standards at several points reveals zero and gain errors; blanks and background runs measure additive contributions. Carry uncertainties of the standards and fitted corrections into the final result.

Reversal provides a particularly transparent test. If $D_{+}=S+b$ and $D_{-}=-S+b$, the combination $(D_{+}-D_{-})/2$ isolates $S$ while $(D_{+}+D_{-})/2$ measures the measurement zero offset. Interleave and repeat the reversals so drift can be detected. The cancellation assumes a stable measurement zero offset and a genuinely odd signal; a common gain error remains. null measurements and bridge comparisons can reduce dependence on detector gain. Swapping sensors or leads exposes asymmetry; checking alignment, shielding, grounding and temperature control tests specific unwanted couplings. Varying signal amplitude or measurement frequency can reveal heating, nonlinear response or pickup.

Use independent measurement principles where possible, and test the predicted response to each deliberate change. Repeated readings alone do not remove a fixed systematic error. Correct identified significant effects and retain uncertainty for imperfectly known corrections; complete elimination cannot generally be demonstrated. Type A and Type B classify how uncertainty is evaluated, rather than simply meaning random measurement errors and systematic errors.^[ref]

A systematic error is a reproducible displacement caused by the apparatus, procedure or measurement model. Repeating the same experiment can determine its mean very precisely while retaining that displacement. Reducing random measurement error therefore does not establish accuracy.

Start with a measurement model listing the response, zero offset, calibration scale and relevant environmental corrections. Compare the instrument with traceable standards at several points across its working range; a single zero check cannot reveal a gain error or nonlinearity. Measure blanks and backgrounds using the same acquisition chain as the signal. Repeat calibration before and after the run to detect drift, and retain the uncertainty of every applied correction.

systematic-error cancellation by reversal separates a wanted effect from an offset with a different symmetry. For example, a resistance measurement with thermoelectric offset $V_0$ gives $V_{+}=RI+V_0$ and $V_{-}=-RI+V_0$. The difference givescancelling a stable offset, while $(V_{+}+V_{-})/2$ diagnoses it. Alternate the current direction quickly enough that the offset is approximately unchanged. This does not cancel a current-scale calibration error or resistance changes from Joule heating; vary the current magnitude and monitor temperature to test those effects. Control measurements must target the actual proposed bias.

Randomizing the order of settings prevents slow drift from consistently imitating a physical trend. Interleaving a reference measurement helps track that drift. Reversing geometry, exchanging matched detectors and changing alignment can expose effects tied to one part of the apparatus. Repeating the measurement with an independent method is particularly valuable when its dominant biases differ; agreement between two instruments sharing the same calibration does not test a common calibration error.

Apply justified corrections, propagate their uncertainties and include correlations when the same calibration enters several observations. A systematic component shared by all observations does not shrink as $1/\sqrt{N}$. A measurement uncertainty budget should distinguish the physical origin of each effect from the method used to evaluate its uncertainty: statistical Type A and other Type B evaluations do not simply mean random and systematic, respectively.^[ref]

The central estimate is $c=4.15/1.93-1=1.15026$. With independent input standard errors, the ratio-minus-one uncertainty from first-order uncertainty propagation isThe four independent reported results cluster near1.21, within about one quoted standard error of this estimate, so they are consistent at this precision. Their tight clustering is not evidence that this measurement has the same precision. If the measurements of a and b were correlated, a covariance term $-2a\mathrm{Cov}(a,b)/b^3$ would be needed. ^[ref]