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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.

A leave-one-out interlaboratory consensus estimates a benchmark for laboratory $i$ from the other participants. For a fixed weighted mean with positive coefficients $w_j$ and at least two results, defineThe omitted result does not enter its own benchmark. This removes the direct participant influence on a consensus assigned value for the stated fixed-weight estimator. It does not establish independence when laboratories share a systematic error. The proficiency-testing guide distinguishes participant consensus from expert or independent reference assignment; the leave-one-out construction here is a mathematical comparison, not a claim that the guide requires this scoring procedure.^[ref]

With equal weights and original results $(9,10,11)$, the omitted-result arithmetic means are $(10.5,10,9.5)$, giving residuals $(-1.5,0,1.5)$. Residuals from the full arithmetic mean are $(-1,0,1)$. In general,The larger magnitudes reflect a changed comparator rather than a changed laboratory result.

The joint dependence of leave-one-out consensus residuals matters when interpreting the collection of scores. Suppose the $X_i$ are independent random variables with a common mean and finite common variance ${\sigma }^2$. A result is independent of the arithmetic mean of the other results, soFor different participants, however, their leave-one-out benchmarks overlap. Linearity of covariance givesThus independent laboratory observations do not give independent residual scores. The zero-sum identity makes their covariance matrix a singular matrix. For $n=4$ and $\sigma =2$ units, each residual has standard deviation $\sqrt{16/3}\approx 2.31$ units, and each distinct pair has correlation coefficient $-1/3$. No normal distribution assumption is needed for these moment identities.

A leave-one-out inverse-variance weighted consensus applies an inverse-variance weighted mean to the remaining participants when their standard uncertainties are known. Under independent unbiased observations with weights $w_j=u_j^{-2}$, put $W={\sum }_j{w}_j$. ThenThese are uncertainties under that observation model; the resulting quantity should not automatically be called a proficiency-test z-score with a fixed assessment scale. Computing $({\sum }_j{w}_j{x}_j-w_i{x}_i)/(W-w_i)$ is algebraically convenient, but direct summation of the remaining terms can avoid cancellation when one participant dominates the total weight.

If a robust estimator has weights depending on the full data set, merely deleting one weighted numerator term need not reproduce the estimator fitted without that participant. The estimator must be recomputed according to its specified rule. Finally, a common additive calibration error remains undetectable in these residuals: replacing every $X_i$ by $X_i+B$ leaves all $D_i$ unchanged. Shared bias in an interlaboratory consensus therefore remains a separate question from self-influence and the variance of the residuals.

A proficiency-test z-prime score modifies a proficiency-test z-score to include uncertainty of the assigned value in proficiency testing:Here ${\sigma }_{\mathrm{pt}}>0$ is the standard deviation for proficiency assessment, and $u(x_{\mathrm{pt}})$ is the assigned value's standard uncertainty. The latter describes uncertainty in the comparator, not the participant's reported uncertainty.^[ref]

An original example gives $x_i-x_{\mathrm{pt}}=3.6$ units, ${\sigma }_{\mathrm{pt}}=1.2$ units and $u(x_{\mathrm{pt}})=0.9$ units. The ordinary score is 3; the modified denominator is $\sqrt{1.44+0.81}=1.5$ units and $z_i^{\prime }=2.4$. Under the guide's common interpretation, the first reaches the action boundary and the second lies in the warning interval. The change reflects benchmark uncertainty, not a change in the measured result.

The effect can be separated algebraically. Put $r=u(x_{\mathrm{pt}})/{\sigma }_{\mathrm{pt}}$. ThenWhen $r=0.3$, the magnitude is about 0.958 times the ordinary score. The guide treats a contribution below $0.3{\sigma }_{\mathrm{pt}}$ as negligible for ordinary score interpretation. Conversely, when $r$ becomes very large, the modified score tends toward zero for any fixed deviation. A weak benchmark can therefore make a discrepancy difficult to interpret; it cannot make a measurement intrinsically accurate.^[ref]

Do not replace the assessment scale with the laboratory's uncertainty and still call the result this score. An uncertainty-normalized compatibility calculation asks a different question. Likewise, if the assigned value uses the participant's own result, participant influence on a consensus assigned value creates dependence that must be understood before interpreting the score as an independent check.

The scheme should state its score and benchmark treatment in advance. When assigned-value uncertainty is too large, options include improving the reference assignment or explicitly reporting its limitations. Enlarging a denominator after seeing an inconvenient result does not supply new measurement evidence.