Abstract

Given a set of representative samples of aggregate source material (e.g. from a stockpile or production line), it is shown that if only percentaged data is reported, the mean composition of the percentaged data is best calculated using a robust estimate of the logratio mean. However, if raw mass data is also available (as will generally be the case), then it is best to first calculate the average of the constituent masses, and then to convert these into a percentage. The difference between confidence, prediction and tolerance intervals is reviewed and it is shown that the repeatability of aggregate samples and an effective long-term ‘detection limit’ for constituents can be reliably estimated using duplicates of run-of-the-mill samples; an upper boundary can be placed on the detection limit by application of, say, a two-sided {95%, 99%} tolerance interval. Numerical approximations are given to enable a variety of normal two-sided tolerance factors to be computed.

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