Which practices verify data integrity before including it in a report?

Which practices verify data integrity before including it in a report?

• A. Check sources, triangulate data, verify calculations, cite metadata, and document data collection methods and limitations
• B. Rely on a single source and skip metadata
• C. Verify data by memorizing numbers
• D. Ignore data collection methods

Answer: (A)

Verifying data integrity comes from building trust through provenance, accuracy, and transparency. Checking sources ensures you’re pulling from credible origins rather than questionable claims. Triangulating data means comparing data using different methods or sources to see if they converge, which reduces the chance that a single flawed point skews the results. Verifying calculations catches arithmetic mistakes, formula errors, and improper aggregations that can distort findings. Citing metadata preserves the context of the data—where, when, by whom, and under what conditions it was collected—so others can assess relevance and reliability. Documenting data collection methods and limitations lays out the process and any known constraints, enabling readers to judge whether the data supports the report’s conclusions. Together, these practices create a transparent trail from data to conclusions, essential for integrity. Relying on a single source and skipping metadata misses cross-checks and provenance; memorizing numbers offers no verification; ignoring data collection methods hides biases and constraints.

Verifying data integrity comes from building trust through provenance, accuracy, and transparency. Checking sources ensures you’re pulling from credible origins rather than questionable claims. Triangulating data means comparing data using different methods or sources to see if they converge, which reduces the chance that a single flawed point skews the results. Verifying calculations catches arithmetic mistakes, formula errors, and improper aggregations that can distort findings. Citing metadata preserves the context of the data—where, when, by whom, and under what conditions it was collected—so others can assess relevance and reliability. Documenting data collection methods and limitations lays out the process and any known constraints, enabling readers to judge whether the data supports the report’s conclusions. Together, these practices create a transparent trail from data to conclusions, essential for integrity. Relying on a single source and skipping metadata misses cross-checks and provenance; memorizing numbers offers no verification; ignoring data collection methods hides biases and constraints.