Linked from

The 19 pages that link to Data quality, each with the reason it gives.

• Citizen scienceRelated: Protocols, training, and validation help determine whether public contributions support reliable conclusions.

• Precision agricultureRelated: Faulty field measurements or maps can produce misplaced prescriptions.

• Public health surveillanceRelated: Poor-quality reports can distort trends and misdirect public health action.

• Data governanceRelated: Governance defines quality expectations and assigns responsibility for meeting them.

• Descriptive statisticsRelated: Descriptive results inherit errors and omissions present in the underlying observations.

• Business intelligenceRelated: Poor-quality inputs can make precise BI reports misleading.

• Training dataRelated: Errors and omissions in training examples can be learned as if they were reliable patterns.

• DataRelated: Quality limits how much confidence analysis can place in recorded values.

• Data collectionRelated: Collection procedures shape errors and omissions that affect later data quality.

• Quantitative dataRelated: Poor-quality measurements can undermine conclusions even when analyses are technically sound.

• Scaling lawRelated: Equal token counts can produce different outcomes when the training data differs in quality.

• CrowdsourcingRelated: Crowdsourced datasets can be broad, but uneven expertise and participation affect reliability.

• Decision support systemRelated: Poor input data can undermine apparently precise analyses and recommendations.

• Statistics (numerical data)Related: Quality determines whether a numerical summary deserves confidence.

• Health informaticsRelated: Analyses and clinical tools are only as dependable as the health data they use.

• DataismRelated: Dataist conclusions depend on records whose limitations can be difficult to detect.

• Data setRelated: Quality assesses whether a data set is suitable for its intended purpose.