Although the DQA Process is presented in a linear fashion, it is important to note that it is by nature iterative. For example, if in Step 4 analysts cannot verify assumptions, they may return to Step 3 to re-examine their assumptions and choose a different statistical test. DQA Process Step 1. In this step, investigators review the DQO outputs and any data collection documentation for consistency. If DQOs were not developed, the investigators define the statistical hypothesis and specify tolerable limits on decision errors.
Investigators review the objectives of the study to provide a context for analyzing the data.
If a planning process such as the DQO Process was implemented before the data were collected, then this activity consists of reviewing the documentation of the study objectives. Translate objectives into statistical hypotheses. In this activity, the investigators use their study objectives to develop a precise statement of the primary hypotheses to be tested using environmental data. If DQOs were developed, the statement of hypotheses can readily be developed from the decision rule in the outputs of Step 5 of the DQO Process and the baseline condition identified in Step 6 of the DQO Process . If DQOs were not developed, then the analyst should consult with the data user to develop hypotheses that address the data user's concerns.