Normative variance functions can be used to accurately predict sampling exigencies, but such empirically derived formulae are continuous functions that can predict levels of sampling precision that cannot logically occur in discrete population samples. General formulae are presented that allow calculation of upper and lower boundary constraints on levels of sampling precision. These boundary constraints would only have a significant influence on sampling design where populations are so sparse that samples consist mainly of presence-absence data. A previously published empirical equation for the prediction of requisite sample number for the estimation of a freshwater benthos population correctly shows that using a small sampler can result in an up to 50-fold reduction in the amount of sediment processed, regardless of these constraints. A previously published empirical equation for the prediction of sampling variance, based on over 3000 sets of replicate samples of marine benthos populations, suggests that the use of small samplers over large ones requires the processing of between one-half and one-twentieth of the sediment for the same level of precision. It is concluded that discussions of sampling optimization should be based on knowledge of real sampling costs.