ABSTRACT
Parametric tests assume that the data have some distributional form, whereas nonparametric tests make no distributional assumptions. The term data quality assessment (DQA) refers to the five-step EPA process that provides a comparison of the implemented sampling approach and resulting analytical data against the sampling, data quality, and error tolerance requirements specified by the DQOs. Those steps are: Review DQOs and Sampling Design, Conduct Preliminary Data Review, Select the Statistical Hypothesis Test, Verify the Assumptions of the Statistical Hypothesis Test, and Drawing Conclusions from Data. The DQA process is designed to evaluate statistically based sample designs. Obtain a copy of the DQA checklist, project DQO summary report, sampling and analysis plan, data verification/validation packages, maps showing final sampling locations, and any design change notices. The overall performance of the sampling shall be evaluated by performing a statistical power calculation on the statistical hypothesis test over the range of possible parameter values.
