Measure the observed survey record error rate from an audit and place a Wilson confidence interval around it. Errors may include invalid responses, coding mistakes, duplicates, or any consistently defined audit failure. The tool also converts the rate to errors per thousand records and estimates the clean-record count, helping teams communicate quality without inventing a universal pass-or-fail benchmark.
Observed error rate = errors/audited. The upper uncertainty bound uses the Wilson score interval for a binomial proportion.
The primary result is conditional on the values and statistical assumptions entered. Read the secondary results to understand uncertainty, workload, or design sensitivity before making a decision.
This planning estimate is not a substitute for a study protocol, domain review, or a method chosen for the final data distribution.
An audit finding 18 error records among 600 records has a 3.00% observed error rate, equivalent to 30 errors per 1,000 audited records.
The calculator uses a standard statistical approximation. Small samples, highly skewed outcomes, or model misspecification can make the estimate less accurate.
Yes. A 90% setting gives a narrower interval or lower sample requirement, while a 95% setting provides more conservative coverage.
Use zero only where the input allows it. A rate calculation still needs a positive total or a defined prior so that division by zero is avoided.
No. It summarizes uncertainty or planning assumptions; study design, randomization, measurement quality, and confounding still matter.
Keep rates and intervals at the shown precision, but round required counts upward because a fraction of a sample or processing unit is not usable.
| Item | How it is used |
|---|---|
| Observed or planned sample | Sets the available evidence or workload. |
| Uncertainty setting | Controls interval width or rejection threshold. |
| Variability or rate | Translates the sample into uncertainty, power, or capacity. |