Fitness for purpose is the principle universally accepted among analytical scientists as the correct approach to obtaining data of appropriate quality. Yet few analytical scientists or end-users of data are in a position to specify exactly what quality of data is required for a specific task. A definition of fitness for purpose based on minimal expected loss is proposed in this paper. This idea enables one to develop optimal strategies for apportioning resources between sampling and analysis, and for balancing technical costs with end-user losses due to error.
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Thompson et al. (1996) studied this question.
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