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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.