Basic theory of measurement and error – Measurement quality

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• Measurement quality
• Measurement error
  • Measurement error analysis
  • Random and systematic errors
• Measurement accuracy
  • Comparability & Commensurability
  • Concept system for ‘Quantity’
• Specifications of Process and Measurement Capabilities
• Uncertainty in a measurement value
  • Error models and measurement uncertainty
• Estimating measurement uncertainty
• Conformity assessment
  • Decisions

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Measurement quality

Measurements are made in order to increase our knowledge about reality and to provide bases for decisions. The quality of the decisions is not normally better than the quality of facts they are based on.

One difference between a measurement result and many other products, for example, nails, is that there is usually no prior specified value for a quantity; the measurement value should be as close as possible to a true but unknown value. As a “consumer” of measurement results, one as has therefore often little opportunity to judge the quality of the measurement results. Instead one has to refer to the specifications of the “producer” (the laboratory). A measurement result should therefore contain such information about the uncertainty which is necessary for a correct interpretation and judgement in making a decision. The quality of a measurement result is thus determined both by the structure of errors for the measurement process used as well as our knowledge of this error structure

The particular part of the uncertainty which is relevant depends often on the actual use of the measurement results and to make the information useful it is obviously necessary that the customer knows how it should be used. Information about the measurement uncertainty which cannot be used by the customer in a real way does not contribute to the quality of the decisions to be made, that is, the information on the measurement uncertainty has to be related to both the customer’s needs and knowledge.

In order to improve the quality of the measurement values, the possible errors involved have to be reduced and, to improve the quality of the estimates of measurement uncertainty, knowledge of the possible errors should be increased. Increased knowledge of the error structure can often lead to an increase in the estimated uncertainty since one appreciates that more sources of error have to be taken into consideration. A larger estimated uncertainty can indeed be more realistic and therefore has itself a higher quality.

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