Why We Need the Theory of Sampling
Without representative sampling, measurement uncertainty is compromised. Here, we present the current Theory of Sampling versus Measurement Uncertainty debate. The verdict? Nolo contendere!
Kim H. Esbensen , Claas Wagner |
The purpose of sampling is to extract a representative amount of material from a ‘lot’ – the ‘sampling target’. It is clear that sampling must and can only be optimized before analysis. In a recent paper, we show how non-representative sampling processes will always result in an invalid aliquot for measurement uncertainty (MU) characterization (1).
A specific sampling process can either be representative – or not. If sampling is not representative, we have only undefined, mass-reduced lumps of material without provenance (called ‘specimens’ in the theory of sampling) that are not actually worth analyzing. Only representative aliquots reduce the MU of the full sampling-and-analysis process to its desired minimum; and it is only such MU estimates that are valid. Sampling ‘correctness’ (which we define later) and representativity are essential elements of the sampling process.
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