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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)fr, or a ratio of the two values, Xfr, 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 /> ' • Uniform sampling containers (i.e. containers that are supplied, constructed, cleaned and <br /> prepared identically); <br /> ' • Standardized forms and/or electronic data storage devices for recording field and <br /> analytical data, prepared sample identification tags, and Chain-of-Custody (COC) <br /> records; <br /> ' • Field team performance observations; <br /> • Field and laboratory instrument performance; <br /> • Standardized protocols for field and laboratory instrument calibrations using certified <br /> standard solutions; <br /> • Data documentation audits to determine data adequacy; and <br /> ' • Known and validated uncertainties for field and analytical data. <br /> ' varra Companies <br /> Quality Assurance Project Plan <br /> Page 4 <br />