Determination of the complete sampling distribution (Lyman, 2014), as opposed to estimation of the sampling variance, represents a significant advance in sampling theory. This is one link that has been missing for sampling results to be used to their full potential. In particular, access to the complete sampling distribution provides opportunities to bring all the concepts and risk assessment tools from statistical process control (SPC) into the production and trading of mineral commodities, giving sampling investments and results their full added-value. The paper focuses on the way by which sampling theory, via the complete sampling distribution, interfaces with production and statistical process control theory and practice. The paper evaluates specifically the effect of using the full sampling distribution on the Operating Characteristic curve and control charts’ Run Length distributions, two SPC cornerstones that are essential for quality assurance and quality control analysis and decision-making. It is shown that departure from normality of the sampling distribution has a strong effect on SPC analyses. Analysis of the Operating Characteristic curve for example shows that assumption of normality may lead to erroneous risk assessment of the conformity of commercial lots. It is concluded that the actual sampling distribution should be used for quality control and quality assurance in order to derive the highest value from sampling.