Michael H. Ramsey

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.