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based on the end use of the data being collected. DQOs will be described in detail within individual <br /> sampling and analyses plans (SAPS). <br /> Precision, Accuracy, Representativeness, Completeness and Comparability (PARCC) parameters <br /> are indicators of data quality. The end use of the measurement data should define the necessary <br /> PARCC parameters. Numerical precision, accuracy, and completeness goals must be established <br /> in each site SAP and will aid in selecting the measurement methods. <br /> 1.8.1 Data Precision and Accuracy <br /> Precision is a measurement of the reproducibility of a measurement under a given set of conditions. <br /> The closer the numerical values of the measurements, the more precise is the overall measurement. <br /> Precision will be stated in terms of the standard deviation for three or more measurements of the <br /> percent difference for two measurements, depending on the necessary precision of a particular <br /> study. Laboratory precision will be within established control limits for a particular analytical <br /> method, if known. For chemical analyses, laboratory precision will be assessed using laboratory- <br /> spiked samples <br /> Accuracy is defined as the degree of agreement between the measurement and average of <br /> measurements of measurements for a parameter and the accepted reference or true value. It will be <br /> expressed as the difference between the measured value (X) and the reference or true value (T), the <br /> difference in percent between two values, 100(X-T)/T, or a ratio of the two values, X/f, depending <br /> upon the study. Laboratory accuracy will be within established control limits for a particular <br /> method when known. For chemical analyses, laboratory accuracy will be assessed using <br /> laboratory-spiked samples. <br /> 1.8.2 Data Representativeness <br /> Representativeness expresses the degree to which data accurately and precisely represent a <br /> characteristic of a population, parameter variations at a sampling point, a process condition or an <br /> environmental condition. Accurate sample collection requires that samples be undisturbed and <br /> representative of the native substance being sampled. All measurements will be made so that the <br /> results are accurately representative of the media (air, biota, soil or water) and specific time, place <br /> and conditions being measured. <br /> Representativeness of a sample will be ensured by systematic,documented and (whenever possible) <br /> random sampling designs. Project Managers designing SAPS will ensure that, whenever possible, <br /> probabilistic sampling designs are used and that a sufficient number of samples are collected to <br /> meet the DQOs of the project. To minimize the introduction of error during the field program, <br /> general requirements for sample collection and handling have been devised and are described in the <br /> QAPP. Specific procedures will be presented in respective SAPS. <br /> 1.8.3 Data Comparability <br /> Data comparability expresses the confidence factor by which one data set can be compared to <br /> another. All data will be calculated and reported in units consistent with all the sites involved and <br /> the regulatory standards allowing for comparability of databases within the project. Data <br /> comparability will be achieved using standard field and analytical methods or written procedures. <br /> Data with different quality objectives will be compared in a statistically defensible manner outlined <br /> when the data are presented. Factors that will ensure data comparability are summarized as follows: <br /> • Standard procedures for sample collections, <br /> • Standard sample handling and transport: <br /> Varra Companies <br /> Qualify Assurance Project Plan <br /> Page 4 <br />