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In the context of quality assurance strategies, statistical process control techniques and conformance testing are necessary to perform a correct quality auditing of process outcomes. However, data collection is based on measurements and every measurement is intrinsically affected by uncertainty. Even if adopted instruments are in a condition of metrological confirmation, random and systematic measurement errors can not be completely eliminated. Moreover, the consequence of wrong measurement-based decisions can seriously decrease company profits because of larger repairing and shipping costs, as well as for the loss of reputation due to customers' dissatisfaction. This paper deals with a theoretical analysis aimed at estimating the growth in decisional risks due to both random and systematic errors. Also, it provides some useful guidelines about how to choose the test uncertainty ratio of industry-rated measurement instruments in order to bound the risk of making wrong decisions below a preset maximum value.