Estimate the observed error rate in a reviewed data-labeling sample and translate it into an expected number of errors for a larger batch. The tool focuses on straightforward QA monitoring: how many checked labels were wrong, what percentage that represents, and what that rate would imply if it continued across the full batch.
Labeling managers can use the estimate to compare vendors, annotation rounds, or task types on a consistent basis. The projected batch errors are only a planning estimate because error rates can shift with label complexity, annotator experience, instructions, and sampling method. For operational decisions, pair the percentage with the review sample size and investigate whether errors cluster around specific classes or edge cases.
1. Enter reviewed labels
Use the number of items that actually went through QA.
2. Record errors found
Count labels judged incorrect under the review standard used for this batch.
3. Add the full batch size
Enter the larger population for which you want a simple error-count projection.
4. Compare rate and count
Use the percentage for quality comparisons and the projected count for rework planning.
Where:
Assumptions: The projection assumes the reviewed sample is representative of the full batch and that the error rate remains similar across the remaining labels.
The error rate is the share of reviewed labels that failed the QA criterion, with a simple projection to the full batch.
Use representative sampling and consistent review rules if you compare error rates across teams or time periods.
Given:
Calculation:
Error rate = 36 / 800 = 0.045 = 4.50%. Projected errors = 12,000 × 0.045 = 540. Implied accuracy = 95.50%.
Result:
Estimated error rate = 4.50%, with about 540 errors projected across the batch.
Interpretation:
The projection helps estimate rework volume, but a biased QA sample could make the batch-level count too high or too low.
No. It is the observed rate in the reviewed sample. The full-batch projection assumes the sample reflects the rest of the batch.
Use the same adjudication rule throughout the review. If ambiguous items have their own status, exclude or track them separately rather than changing the definition mid-sample.
The calculator will still compute a projection, but that scenario is usually not meaningful for sampling. Normally the full batch is at least as large as the reviewed subset.
The percentage makes quality comparable across batch sizes, while the projected count helps estimate rework, staffing, and turnaround impact.
If every label is classified as correct or incorrect, accuracy equals 100% minus the error rate. More complex QA schemes may track additional outcomes separately.