What is the distinction between validation and verification in data tasks?

• A. Validation checks data reasonableness; verification confirms processes and outputs align with source data.
• B. Validation confirms processes; verification checks data reasonableness.
• C. Validation and verification are the same thing.
• D. Validation is optional and verification is unnecessary.

Answer: (A)

Validation focuses on the data itself—checking that it is sensible, complete, and fits the expected rules or formats before it’s used. It answers questions like: Is this value in a plausible range? Does the date make sense? Does a postal code match the stated country? It’s about whether the data is fit for use.

Verification, on the other hand, checks the processes and the outputs produced from those processes against the source data. It asks: Did the data processing steps run correctly, and do the results accurately reflect the original data? It’s about ensuring the transformation or computation is faithful to the inputs and that the resulting outputs are trustworthy.

So the best description states that validation checks data reasonableness, while verification confirms that the processes and the outputs align with the source data. The idea isn’t that they’re the same, and it isn’t that one is optional; one is about data quality, the other about correctness of processing and results.