EPA-QUALITY: EPA/240/B-06/003
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Title
| Data Quality Assessment: Statistical Methods for Practitioners |
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Author
| U.S. Environmental Protection Agency |
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Abstract/Summary Statement
| Data Quality Assessment (DQA) is the scientific and statistical evaluation of environmental data to determine if they meet the planning objectives of the project, and thus are of the right type, quality, and quantity to support their intended use. This guidance describes broadly the statistical aspects of DQA in evaluating environmental data sets. This guidance applies to using DQA to support environmental decision-making (e.g., compliance determinations), and to using DQA in estimation problems in which environmental data are used (e.g., monitoring programs). This is technical document to help assess data quality criteria and performance specifications for decision making. By using DQA, a reviewer can answer four important questions: 1. Can a decision (or estimate) be made with the desired level of certainty, given the quality of the data? 2. How well did the sampling design perform? 3. If the same sampling design strategy is used again for a similar study, would the data be expected to support the same intended use with the desired level of certainty? 4. Is it likely that sufficient samples were taken to enable the reviewer to see an effect if it was really present? |
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Table of Contents
| The guidance describes five interative steps for data quality assurance, which are briefly summarized below. 1. Review the project¿s objectives and sampling design. 2. Conduct a preliminary data review. 3. Select the statistical method. 4. Verify the assumptions of the statistical method. 5. Draw conclusions from the data. |
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Citation
| U.S. Environmental Protection Agency, 2006, Data Quality Assessment: Statistical Methods for Practioners: EPA QA/G-9S. Office of Environmental Information, EPA/240/B-06/003, Feb. 2006. |
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Method Source
| EPA-QUALITY |
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Source Organization Country
| USA |
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Publication Year
| 2006 |
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Special Notes
| EPA QA/G-9S is a technical document about data quality assessment; whereas, EPA QA/G-9R is non-technical in that it provides general guidance. |
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Item Type
| Report / Guidance Document |
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Publication Source Type
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Government Agency (Federal, USA) |
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Purpose
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Data analysis |
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Design or Data Analysis Objectives
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Communities & populations Compare locations Exploring/summarizing data Relationships & correlations Revisit Spatial trends Temporal trends |
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Complexity
| High |
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Media Emphasized
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Agricultural Products Air Animal Tissue Biological Dredged Material Groundwater Soils/Sediment Surface Water |
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Media Subcategory
| |
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Special Topics
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Assessing and managing autocorrelation Characterizing the uncertainty of an estimated value Evaluating whether data follow a certain (e.g., normal) distribution Handling nondetects Identifying outliers |
