Sampling Uncertainty/Sampling Quality Expert Working Group

Aims and Intentions

Primary sampling can useful be considered to be the first step in analytical measurement and can often contribute the largest component to the overall uncertainty of measurement. The quality and appropriateness of the sampling procedure is however, often not considered by analytical scientists. The aims of the Sampling and Uncertainty Expert Working Group are therefore to

  • encourage analysts to include primary sampling in uncertainty budgets for the whole measurement process 
  • provide information on methods for the evaluation of the uncertainty associated with sampling protocols 
  • assess existing procedures of evaluating methods of sampling and sample preparation, and encourage the development of improved procedures 
  • encourage best-practise in ensuring sampling quality that is fit-for-purpose 
  • ensure international applicability of guidance from the Expert Working Group by collaboration with appropriate organisations 
  • clarify who needs to be responsible for the quality of sampling and enable their effective communication with the analysts.


Membership of the Sampling Uncertainty Expert Working Group as of 1st May, 2019:

  • Prof. Mike Ramsey  (Chairman)  University of Sussex
  • Bob Barnes Environment Agency
  • Lawrence Bilham UKAS
  • Steve Ellison LGC
  • Chelvi Leonard Food Standards Agency
  • Jenny Lyn Consultant (Corresponding Member)
  • Peter Rostron University of Sussex
  • Prof Mike Thompson Birkbeck College
  • Tom Green  Suffolk Coastal Port Health Authority (Corresponding Member)
  • Roger Wood OBE Secretary


The following has been published under the auspices of the Sub-Committee:


  • Ramsey M.H., Ellison S. L. R., and Rostron P.(eds.) (2019) Eurachem/EUROLAB/ CITAC/Nordtest/ AMC Guide: Measurement uncertainty arising from sampling: a guide to methods and approach, Second Edition, Eurachem, ISBN 978-0-948926-35-8 

Downloadable Files

Sampling Uncertainty 2019
Measurement uncertainty arising from sampling A guide to methods and approaches
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Technical Briefs of the Analytical Method Committee

  • Technical Brief 19: Terminology - the key to understanding analytical science. Part 2: Sampling and sample preparation (2005)
  • Technical Brief 20: Analytical and sampling strategy, fitness for purpose, and computer games (2005)
  • Technical Brief 32: Optimising your uncertainty - a case study (2008) 
  • Techncial Brief 31: Measurement Uncertainty arising from sampling: the new Eurachem Guide (2008)
  • Technical Brief 16A: What is uncertainty from sampling, and why is it important? (2008) 
  • Technical Brief 40: The Duplicate Method for the estimation of measurement uncertainty arising from sampling (2009)
  • Technical Brief 42: The importance, for regulation, of uncertainty from sampling (2009)
  • Technical Brief 51: Quality Control of routine sampling in chemical analysis (2012)
  • Technical Brief 58: Estimating sampling uncertainty – how many duplicate samples are needed? (2014)
  • Technical Brief 60: Random Samples (2014)
  • Technical Brief 64: Unbalanced robust ANOVA for the estimation of measurement uncertainty at reduced cost. (2014)
  • Technical Brief 71: Sampling theory and sampling uncertainty (2015)
  • Technical Brief 73: Representative sampling? Views from a regulator
    and a measurement scientist (2016)
  • Technical Brief 78: Proficiency testing of sampling (2017)
  • Technical Brief 84: Beam sampling: taking samples at the micro-scale (2018)
  • Technical Brief 88: Why do we need the uncertainty factor? (2019) 

AMC Technical Briefs

The new AMC Technical Briefs, will bring to every member of the Analytical Division up to date news of current technical issues.



A stand-alone program, running in Windows, to execute robust analysis of variance with nested data.

Minitab 14 macro for 'Goldmine'

A game to demonstrate the selection of optimum uncertainties for sampling and analysis.


A stand-alone program, running in Microsoft ™ Excel, to execute robust and classical analysis of variance with nested data. Suitable for both balanced designs