For most sites, contaminants in soil exhibit some degree of spatial relationship, meaning that often variance in the concentration reduces as the distance between sample locations decreases. It is well established that strong spatial relationships can reduce the effective sample size of a data set because each sample provides some redundant information (Cressie 1993). In statistical terms, this redundancy violates the assumption that observations are independent. ISM confidence intervals generated from spatially related data can be too narrow, resulting in a higher frequency of decision errors. Spatial relationships may also introduce bias in estimates of the mean and variance, depending on the sampling protocol. Bias can be reduced by using a truly random sampling strategy, e.g., simple random sampling. The issue of spatial relationships applies to discrete as well as ISM sampling.