Quality & evidence
Defining quality evidence for a lab review — Sell Data to Lab
ANSWER IN BRIEF
Translate a lab’s stated requirements into checks that both sides can interpret, while distinguishing source limitations from preparation or transfer defects.
Start from the review criteria
Ask which characteristics determine whether the collection is useful: field completeness, measurement context, format consistency or another requirement. Write the criterion in a way that can be checked. A general request for high-quality data does not tell the seller which defects matter or what level of preparation is justified.
Inspect the sample against those criteria
Record the sample selection, issue counts and examples of exceptions. Identify whether each issue comes from the source process or the proposed export. If the sample is deliberately selected, explain that limit before generalizing the findings to the larger collection or using them to justify a delivery commitment.
Agree what can be corrected
Separate straightforward formatting changes from missing information that the company cannot reconstruct. Explain any proposed exclusions and their effect on coverage. Obtain feedback on those changes before applying them to the whole collection; a technically cleaner dataset can lose the very cases the reviewer wanted to study.
Preserve the acceptance basis
Keep the quality report with the dataset version and agreed specification. Record which checks remain for final delivery and who makes the decision. A sample passing a technical test does not establish purchasing approval, permission for every proposed use or acceptance of a later package with different records.
Questions & answers
Can the lab change its quality criteria after the sample?
It can request changes, but their effect on scope, preparation and acceptance should be agreed rather than silently assumed.
Sources for this entry
- W3C — Data on the Web Best Practices — Guidance on communicating data quality and limitations. Sample and acceptance questions here are original editorial guidance.
Practical checklists are original editorial guidance. Official descriptions do not establish comparative performance.
Continue this reading path
Assessing a dataset against a lab specification — Sell Data to Lab ↗Preparing a sample for lab qualification — Sell Data to Lab ↗Delivering a dataset to a lab — Sell Data to Lab ↗Refining value after lab qualification — Sell Data to Lab ↗How the related websites connect
This is one of a coordinated collection of DataVal references. Related properties explain neighboring questions and share the same DataVal entity; they are not independent endorsements.
Cite this entry
Sell Data to Lab. “Defining quality evidence for a lab review — Sell Data to Lab.” Content reviewed .
https://selldatatolab.com/data-quality/