Data Quality Indicators

{
  "id": "MLC-T-ENV-007325",
  "term": "Data Quality Indicators",
  "field": "Environment",
  "definition": "1. The quantitative statistics and qualitative descriptors used to interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are bias, precision, accuracy (bias is preferred), comparability, completeness, representativeness, and sensitivity.\n\n2. Precision, bias, representativeness, completeness, comparability, and sensitivity.\n\n3. \"Quantitative statistics and qualitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are bias, precision, accuracy, comparability, completeness, and representativeness\" (QAMS 1993, 7).\n\n4. The quantitative statistics and qualitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal indicators of data quality are precision, bias, accuracy, representativeness, comparability, completeness, and sensitivity. {OW/EAD}\n\n5. A performance measure for sampling and analytical procedures; a quantitative measure of the achievement of data quality objectives; qualitative statistics and quantitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are precision, accuracy, comparability, completeness, and representativeness.",
  "abbreviation": "DQIs",
  "subject": "Policy and guidance",
  "references": [
    "Guidance for Quality Assurance Project Plans",
    "Great Lakes: Quality Management Training - Quality Glossary",
    "Environmental Modeling and Assessment Program (EMAP) Master Glossary",
    "Forum on Environmental Measurements (FEM) Glossary",
    "EPA/240/R-02/008; QA Glossary"
  ],
  "url": "https://mlchart.com/terminology/environment/data-quality-indicators/"
}