ABSTRACT
Measurements play a dominant role in quantitative analysis and are subject to experimental error. Uncertainty in the results can be minimized, but never completely eliminated. This trait is inherent in any fi eld where numerical results are obtained. It is especially important in environmental analysis where large datasets from repeated measurements of a sample or group of samples are obtained to understand error sources and treat uncertainty. Before we delve into the types of error encountered in environmental analysis, let us fi rst step back and consider the sample in both a statistical sense and an environmental sense.
