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SOP #12 <br /> Date:9/1/93 <br /> Page 25 of 31 <br /> 9 .0 <br /> QUALITY ASSURANCE REQUIREMENTS <br /> 9.1 DATA QUALITY OBJECTIVES <br /> The quality of the data collected in the field can be adversely affected by errors in <br /> collection or observation. Establishing appropriate levels of control over sources of <br /> error and quantifying the errors, when possible, will assist in assessing the impact of <br /> errors on the project. Data quality objectives are expressed in terms of accuracy, <br /> precision, completeness, comparability, and representativeness. <br /> Accuracy is a measure of the bias in a system. It is the degree of agreement of a <br /> measurement (or an average of measurements of the same thing) with the accepted <br /> reference or true value. The exact bias of a system will never be known since the <br /> true values are not accessible. <br /> Precision is a measure of mutual agreement among individual measurements of <br /> the same property, usually under prescribed similar conditions. The measurement <br /> of precision is made through the use of duplicate or replicate samples, taken at <br /> regular, specified intervals. <br /> Comparability expresses the confidence with which one data set can be compared <br /> with another. Comparability can be related to precision and accuracy as these <br /> quantities are a measure of data reliability. <br /> Accuracy, precision, and comparability are more applicable for quantifiable sample <br /> analyses (see the QAPP), so for this SOP, the data quality objectives will be stated in <br /> terms of representativeness and completeness. <br /> Representativeness is the degree to which a set of data accurately represents the <br /> characteristics of a population, a process condition, or an environmental condition. <br /> Representativeness will be assured by performing the sampling and observations in <br /> accordance with this SOP. <br />