The scope of environmental issues and the consequences of attendant remediation decisions involve the combination and cross-comparison of information from multiple sources and types. Environmental data collection is a difficult and expensive enterprise, necessitating reuse of available data, often for purposes unintended at the time a single environmental issue often need to be compiled from several sources involving different variables, measurements, time and spatial scales, accuracy, precision and completeness. Design criteria for data collection may be incomplete or unavailable, and selection bias is often present but difficult to quantify. Data validation can be difficult and complex, involving piecemeal cross-comparisons among several data sources. Environmental data sets are often large and consequently difficult and expensive to manipulate and analyze. As demonstrated in this chapter, these problems pose important challenges to statistical science, and statistical methods are central to their solution.