In this second in a series of articles on individual aspects of the assessment of uncertainty of measurement, Stephen MacDonald continues with an assessment of the impact of assay modelling.
Following on from defining the measurand in the first article in this series,1 we now move on to assay modelling and characterising the measurement. Assay modelling is the process of deciding how we assess measurement uncertainty (MU). Simply put, the scientist judges which of the methods available for assessment best describes an individual assay (specific methods will be discussed in subsequent articles). It is at this point that we begin to collect and apply the tools (in the form of data sources) we think represent our assay uncertainty.
The process identifies and categorises uncertainty sources, collects the data that we have available, and uses those data to describe and quantify the uncertainty. There should be no need for additional data generation in this process. All data required should be readily available in all laboratories. There is also no requirement for any calculations…yet.
To begin, it is easiest to think about what questions we are trying to answer. For example:
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