4.2 Uncertainty in Estimates of the Decision Unit Mean

Even the most comprehensive sampling protocols introduce some degree of sampling error. Therefore, one challenge in developing sampling designs is to balance the potential for decision errors against the practical constraints of site investigations, including having incomplete information about potential source locations, as well as time and budget limitations. The objective of ISM is to provide a reliable estimate of the average (i.e., arithmetic mean) contaminant concentration in a DU, recognizing that any individual ISM sample may over- or underestimate the mean to some degree. This sampling error may be attributed to a variety of factors. A principal objective of systematic planning of most sampling designs is to minimize the major sources of error in both the field and the laboratory. In practice, the estimated variance is often viewed as an overall measure that includes the contribution of many sources of error. Just as with discrete sampling, the estimated variance can be used to quantify a UCL for the mean for ISM samples and the same UCL equations apply. This section describes important concepts relevant to characterizing variance in ISM sampling. Section 4.3 builds from these concepts by presenting the results of simulation studies that examine the performance of alternative ISM sampling strategies applied to a wide range of theoretical site conditions.