Choose full review or sampling based on the intended use

Define what the review must establish

Choose the review method around the intended use so any time saving still produces a result you are prepared to rely on.

In ParseMill, select an extracted invoice field to inspect its source, correct the value and export the reviewed workbook. Try your own invoices as well as the interactive sample. Compare upload, checking and correction time with your current way of building the same spreadsheet.

For a one-off register feeding consequential decisions, the workflow owner may require every critical field to be checked. For an exploratory tool comparison, a carefully designed sample may be appropriate. Record that decision and its rationale rather than using convenience alone.

Use a review-scope matrix

These synthetic examples describe scope, not statistical guarantees. A sample size of twenty has no universal meaning without a sampling design, population definition and decision objective. Avoid presenting an arbitrary percentage checked as a confidence level.

UK Government: Data Quality Framework relates quality assessment to intended use. Let the recipient’s actual risk and information needs drive which fields require complete review.

Document the selection method

1. Define the population using source versions, documents, pages, rows and required fields. Record exclusions before selecting items.
2. Choose full review or a stated sampling method. For sampling, record how items were selected and what conclusions the design can support.
3. Include targeted checks of known difficult cases separately from any probability sample. Do not combine them into an unexplained estimate.
4. State escalation rules when defects appear, such as reviewing the entire affected table or document class. Keep unchecked scope visible in the release note.

scikit-learn: Metrics and scoring stresses alignment between evaluation and purpose. Report denominators and selection methods so a measured result can be interpreted correctly.

Avoid overextending a clean sample

Finding no errors in selected rows does not prove that the rest are error-free. A layout defect may affect a page or supplier absent from the sample. Review source diversity and known failure modes when deciding whether additional checks are needed.

A targeted sample of easy, clear documents is useful for initial setup but weak evidence for difficult scans. Label it honestly. If you need a statistical assurance statement, obtain a sampling design appropriate to the task rather than inventing one from a convenient row count.

Make the handoff claim precise

For the second fixture, write that twenty identified rows were checked and eighty were not individually reviewed. Include the selected row list and any exceptions. A recipient can then decide whether the evidence is sufficient for their use.

When evaluating ParseMill, agree the review scope in advance. Track full checks, sampled checks and unresolved records separately so the adoption decision reflects what was actually examined.