How to use this calculator
- Enter the number of independently reviewed items.
- Enter how many met the stated criterion.
- Select the confidence level.
- Calculate and compare both bounds with your decision threshold.
#3161 · AI & Technology Tool
Data Quality Confidence Interval Calculator
Estimate a statistically defensible range for a data quality pass rate. Enter the number of records inspected, how many passed the same validation rule, and the confidence level. The calculator uses a Wilson score interval, which behaves better than the simple normal approximation when quality is very high, very low, or based on a modest sample. Use the range—not only the point estimate—when comparing a release threshold or deciding whether more inspection is needed.
Calculator
Sample evidenceFormula
Wilson interval = adjusted center ± adjusted margin
The observed proportion is x ÷ n. The adjustment uses the selected confidence z-score and keeps both limits within 0% and 100%.
What the result means
The main result is the observed rate. The lower and upper bounds describe sampling uncertainty under the stated confidence level; they do not include bias from how items were selected or judged.
Use one consistent acceptance rule and a representative sample. A narrow interval cannot correct a biased review process.
Example calculation
With 970 valid records out of 1,000 at 95% confidence, the observed quality rate is 97.00% and the Wilson interval is approximately 95.73% to 97.90%.
Tips for better results
- Define the validation rule before sampling.
- Sample across relevant sources and time periods.
- Avoid counting duplicate or dependent items as independent.
- Use the lower bound for conservative threshold checks.
- Increase the sample size when the interval is too wide.
Frequently asked questions
Why does this calculator use the Wilson interval?
Wilson intervals remain bounded between 0% and 100% and generally perform better than a basic normal interval for small samples or rates near the extremes.
Can I enter a 100% pass rate?
Yes. The interval will still show uncertainty below 100%, reflecting that a finite sample cannot prove every future item will pass.
Should the sampled items be independent?
Ideally, yes. Repeated or clustered items can make the interval look more precise than the underlying evidence supports.
What happens if I choose 99% instead of 95% confidence?
The interval becomes wider because a higher confidence level requires more coverage of plausible underlying rates.
Can I compare the lower bound with an internal threshold?
Yes, if the threshold and review rule were defined in advance. The lower bound is a conservative value for that comparison, not a guarantee.
Related calculators
Interval inputs
| Variable | Meaning |
|---|---|
| n | Items reviewed |
| x | Valid records |
| p | x ÷ n |
| z | Confidence z-score |