SAP general 13 12/05/2011
As projects utilizing this template are limited in scope, defining action levels and measurement
quality objectives (MQOs) for field and laboratory measurements should be sufficient. The
project manager, or other decision maker identified earlier in the project organization section,
must determine what level of uncertainty is acceptable.
More sophisticated DQO discussions involve defining null test hypotheses and confidence
intervals. These should be considered depending on project decision making needs, but
generally such discussions are not expected to be applicable in limited sampling events. EPA’s
Guidance for Systematic Planning Using the Data Quality Objectives Process (EPA QA/G-4,
February 2006) should be consulted for more information.
In addition to meeting defined DQOs, data quality is also evaluated as to its conformance to
measurement quality objectives (MQOs), which are discussed in the next section.
3.3 Data Quality Indicators (DQIs) and Measurement Quality Objectives (MQOs)
Data Quality Indicators (DQIs) provide a means to evaluate the quality of data and are normally
defined in terms of PARCCS (precision, accuracy, representativeness, completeness,
comparability, and sensitivity (method detection limits). Precision, accuracy, and sensitivity are
usually covered in method specific criteria (see below). However, the other DQIs
(representativeness, completeness, and comparability) should be defined in the plan for the
project as a whole.
The values that are to be assigned to the quantitative data quality indicators (accuracy,
precision, completeness and sensitivity) and statements concerning the qualitative indicators
(representativeness and comparability) are determined by the answers to the question: How
sure are you that the values of the data are what the analyses have determined them to be?
Each DQI needs to have defined data quality acceptance criteria (measurement quality
objectives (MQOs), as well as a means for assessing whether the criteria were achieved.
Whenever possible, it is desirable that the MQOs be expressed in numerical or quantitative
terms, along with one or more associated quality control (QC) samples that will serve as a
means for assessing the DQI.
All the elements of the sampling event, from the sampling design and collection through
laboratory analysis and reporting, affect the quality of the data. Depending on what the
contaminants of concern are, what effect they may have on human and environmental health,