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Much has been published over the last years on finding the best (consensus) reference value (RV) for a given set of laboratory data attained in inter- or key comparisons (the latter with a specific view to the CIPM MRA). However, the discussion is ongoing. Except cases where real reference values are available from other sources, the quality of the (consensus value is of major importance in proficiency testing, method Validation, or reference material certification.
A distribution-free, self-consistent RV estimation approach has been developed which is based on the single criterion of participant's compatibility, at the cost of adjusted uncertainties. The final result is a fully compatible data set with minimum Overall variance, and adjusted uncertainties for those values reported which had to be made compatible with the (key comparison) reference value.
Any outlier discussion becomes obsolete, including considerations concerning different degrees of robustness of the various location and .dispersion estimators available. The penalty for "badly" performing laboratories is a large attributed uncertainty which forces them to either improve the measurement or adjust (i.e. reduce) their performance Claims. The evaluation algorithm is described. An example from a CCQM comparison is given, and consequences for the performance Claims of the participants discussed.
"Ullmann's Encyclopedia of Industrial Chemistry" is the benchmark reference in chemistry and chemical and life science engineering, covering inorganic and organic chemicals, advanced materials, pharmaceuticals, polymers and plastics, metals and alloys, biotechnology and biotechnological products, food chemistry, process engineering and unit operations, analytical methods, environmental protection, and much more.
The present article is part of the topic collection "Environmental Protection and Industrial Safety". It covers plant and process safety, focusing on hazardous materials and process conditions.