 Brian Brookman

Brian Brookman works on analytical measurement quality and proficiency testing. His official 2026 Eurachem workshop biography describes him as an independent consultant and former Chief Scientific Officer of LGC's global proficiency-testing business. It records work on training, development of testing schemes and accreditation.^[ref]

With Ian Mann, Brookman co-edited the 2021 third edition of Selection, Use and Interpretation of Proficiency Testing (PT) Schemes by Brian Brookman and Ian Mann. The editorial page lists him as chair of its working group. The guide explains how laboratories select comparisons, interpret performance and investigate questionable results.^[ref]

An original statistical example shows one reason scheme design matters. Four reported results $100,100,100,140$ have sample mean110 and sample standard deviation20. Assessing the value140 against that mean and spread gives $(140-110)/20=1.5$. If an independently justified assigned value is100 and the required assessment spread is5, the same reported value gives a proficiency-test z-score of8. The participant values have not changed; the reference and criterion have.

This small invented dataset is not a prescription to estimate a real scheme from four participants. It illustrates how an extreme result can affect a participant-derived consensus and spread. The guide discusses assigned values, standard deviation for proficiency assessment and statistical evaluation as distinct design choices. A laboratory should understand those choices before interpreting a score as a simple accuracy ranking.^[ref]

The scientific role of this work is to connect measurement accuracy, measurement uncertainty and externally defined performance criteria. A comparison can reveal a problem, but determining its cause still requires examination of the method, materials, measurement calibration and data handling.