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Nested contact-study allocation chooses how many independent contacts to prepare and how many readings to take within each contact. In a nested wringing-repeatability study, new contacts sample between-wring variability, whereas extra readings of one unchanged contact mainly reduce readout scatter.

For $m$ wrings and $r$ readings per wring, suppose independent between-wring variance is ${\tau }^2$ and independent readout variance is ${\sigma }^2$. The overall mean hasAn original planning model assigns cost $c_w$ to preparing a wring and cost $c_r$ to each reading. With fixed budget $C=m(c_w+r{c}_r)$, a continuous relaxation givesFor positive costs and nonzero $\tau ,\sigma$, differentiating with respect to $r$ yieldsThis balances the cost of new contacts against the benefit of repeated readings. It is not a universal required replication count; integer counts, minimum study sizes and other constraints still apply.

For stipulated costs 9 minutes per wring and 1 minute per reading, with $\tau =2\mathrm{nm}$, $\sigma =6\mathrm{nm}$ and budget 180 minutes, the continuous optimum is nine readings per wring. Ten wrings then fit exactly, giving standard deviation $\sqrt{0.8}\approx 0.894\mathrm{nm}$ for the mean. Eighteen wrings with one reading each give about 1.491 nm; four wrings with thirty-six readings each give about 1.118 nm.

The variance components need pilot information. Wringing-allocation pilot uncertainty can change the preferred allocation, especially if a small pilot wrongly suggests negligible between-wring variation. Include enough independent preparations to assess that level rather than optimizing solely for an assumed mean variance.

The physical contact can also change with time, handling or surface damage. NIST documents dependence of wringing films on conditions and elapsed time.^[ref] Drift, preparation trends and shared reference uncertainty require additional terms; they do not necessarily decrease by adding readings according to this independent-effects formula. State the objective and the constraints with the final design.

A wringing-film covariance model specifies how uncertain effective contact-layer contributions vary together in a gauge block wringing measurement. Individual contact uncertainties do not determine the uncertainty of a stack unless their dependence is also specified.

Let the vector of contact differences be $\mathbf{\delta }=({\delta }_1,\dots ,{\delta }_n{)}^T$ and its covariance matrix be $\Sigma$. For a correction $\Delta ={\mathbf{c}}^T\mathbf{\delta }$, propagation givesThe coefficients encode the actual geometry and calibration subtraction. They need not all be positive. This is the general correlated-input law applied to wringing-film uncertainty.^[ref]

A simple original model writes ${\delta }_j=b+{ϵ}_j$, where one common contribution $b$ has variance $u_b^2$ and independent contact contributions have variance $u_{ϵ}^2$. ThenFor three equal-sign contacts with $u_b=3\mathrm{nm}$ and $u_{ϵ}=2\mathrm{nm}$, the variance is $93\mathrm{n}{\mathrm{m}}^2$. For a difference of two contacts with coefficients $(1,-1)$, the common contribution cancels and the variance is only $8\mathrm{n}{\mathrm{m}}^2$. These are chosen covariance structures, not universal properties of wrung surfaces.

Calibration and use can share only part of their contact history. If $n$ calibration contacts share effect $b_c$ and $n$ use contacts share effect $b_u$, their common-effect correction is $n(b_u-b_c)$. Its variance isPerfectly shared equal contributions cancel; independent preparation effects do not. Assuming either relation requires evidence about preparation, reference surfaces and measurement history.

Repeated readouts of one unchanged wring mostly probe readout scatter. Rewringing samples a different level of variability; changes of surface preparation or elapsed time may add another. The NIST handbook documents that contact layers depend on conditions and can evolve after thermal equilibration.^[ref] A nested wringing-repeatability study can help separate those levels, but repeatability data alone do not identify every shared calibration contribution.

Retain the covariance assumptions with the result and test whether the proposed matrix is a valid positive semidefinite matrix. A small combined uncertainty obtained by assigning cancelling correlations is meaningful only when those correlations describe the physical comparison.