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Michael H. Ramsey is a researcher credited as chair of the working group and co-editor of the 2019 guide to measurement uncertainty arising from analytical sampling. The editorial page gives his affiliation as the University of Sussex and names fellow editors Stephen L. R. Ellison and Peter Rostron.^[ref]

The scientific problem is the difference between the collected material and the material about which a conclusion is wanted. For a measurand defined over a larger target, excellent measurement repeatability on one extract does not establish small analytical sampling uncertainty. The guide treats collection and analysis as stages of the measurement process.^[ref]

An original measurement model illustrates why replication at the right level matters:Here $\mu$ is the target mean, $S_i$ is the zero-mean deviation associated with independently collected sample $i$, and $A_{ij}$ is zero-mean analytical variation in repeat $j$. Suppose all these effects are mutually independent, with variances ${\sigma }_S^2$ and ${\sigma }_A^2$. Averaging $m$ analyses on each of $n$ collected samples givesFor stipulated variance components ${\sigma }_S^2=9$ and ${\sigma }_A^2=4$ in squared concentration units, three collections analysed twice give variance $9/3+4/6=11/3$, or standard uncertainty about $1.915$ concentration units. Six analyses of only one collection instead give variance $9+4/6=29/3$, or about $3.109$ units. Both plans use six analyses; only the first averages over independently collected material.

The calculation assumes independent collections and an appropriate model for the target; a shared systematic collection error is not removed by increasing $n$. It explains the measurement-design issue addressed by the guide without presenting these invented numbers as Ramsey's experimental observations.

Peter Rostron is a co-editor, with Michael H. Ramsey and Stephen L. R. Ellison, of the 2019 guide to measurement uncertainty arising from analytical sampling. Its editorial page identifies him with the University of Sussex. The guide connects laboratory analysis with the earlier act of collecting material.^[ref]

An illuminating experimental design question is where to place replication. Repeating an analysis of one collected sample reveals a different source of variation from collecting another sample from the target material. A nested design can distinguish the corresponding variance components. The guide also describes an unbalanced sampling-uncertainty design with two analyses of one field sample and one analysis of its field duplicate.^[ref]

An original counting example makes the design concrete. At each of ten targets, two collected samples each analysed twice require $10\times 2\times 2=40$ analytical results. Analysing the first sample twice and its field duplicate once instead requires $10\times (2+1)=30$. The design retains a within-sample analytical contrast at each target and a contrast between separately collected samples, with fewer analytical results. The information content and the appropriate analysis of variance must nevertheless follow the actual replication pattern; thirty results do not automatically have the same precision as forty.

This distinction matters when deciding whether to spend effort on another instrument reading or on another collection. Rostron's verified contribution here is editorial participation in the shared guidance, rather than sole authorship of every method it discusses. Repeated collection can reveal variability without, by itself, proving the absence of a common collection bias.