Calculate the confidence interval for a conversion rate or proportion. Support for 90%, 95%, and 99% confidence levels with sample size inputs.
A confidence interval gives you a range within which the true conversion rate likely falls, rather than a single point estimate. If your observed CR is 3.5% with a 95% CI of [3.1%, 3.9%], you can be 95% confident the true CR is somewhere in that range.
This calculator computes the confidence interval for a proportion (conversion rate) using the normal approximation. Enter your sample size and observed conversions, choose your confidence level (90%, 95%, or 99%), and get the lower bound, upper bound, and margin of error.
Confidence intervals are more informative than significance tests alone because they convey both the size of the effect and the uncertainty around it. They are essential for reporting A/B test results and for understanding the precision of your metrics. Whether you are a beginner or experienced professional, this free online tool provides instant, reliable results without manual computation. By automating the calculation, you save time and reduce the risk of costly errors in your planning and decision-making process.
Point estimates like "our conversion rate is 3.5%" hide the uncertainty inherent in any sample. This calculator reveals the range of plausible values, helping you make decisions that account for statistical uncertainty rather than treating estimates as exact. Having a precise figure at your fingertips empowers better planning and more confident decisions.
p̂ = x / n ME = Z_{α/2} × √[p̂(1−p̂) / n] CI = [p̂ − ME, p̂ + ME] Z values: 90% = 1.645, 95% = 1.960, 99% = 2.576
Result: 95% CI: [3.00%, 4.00%]
With 175 conversions out of 5,000 visitors, the observed CR is 3.50%. The margin of error at 95% confidence is ±0.51%. The true conversion rate is between 2.99% and 4.01% with 95% confidence.
A single number like "3.5% conversion rate" implies false precision. A confidence interval communicates both the estimate and its uncertainty, enabling better decisions. Stating "3.5% ± 0.5% (95% CI)" gives stakeholders an honest picture of what the data supports.
For A/B tests, report the CI of the difference between variants, not just the individual CIs. The CI of the difference directly answers "how much better is B than A?" with the appropriate uncertainty. A CI for the lift of [2%, 18%] is much more actionable than "p < 0.05."
The margin of error is inversely proportional to the square root of the sample size. Practical implication: doubling your sample only reduces the CI width by 29%. To truly narrow uncertainty, you need patience (more data) or higher base rates (test on a macro-metric like add-to-cart instead of purchase).
If you repeated the same experiment 100 times, approximately 95 of the resulting intervals would contain the true value. It does NOT mean there is a 95% probability the true value is in this specific interval (a common misconception).
Increase your sample size. The margin of error decreases with the square root of n. To halve the margin of error, you need 4× the sample. To shrink it by 10×, you need 100× the sample.
For proportions, clip the lower bound to 0%. This can happen with very small samples or very low conversion rates where the normal approximation is less accurate. For better accuracy in such cases, use the Wilson score interval.
Yes. Two proportions' CIs can overlap by up to 25% and still be significantly different in a direct comparison test. Do not use CI overlap as a substitute for a proper significance test.
95% is the standard for most business decisions. Use 99% for irreversible decisions or medical applications. Use 90% for exploratory analysis where you want to detect trends with less conservatism.
No. This calculator uses the Wald (normal approximation) interval, which is simpler and works well for moderate sample sizes and conversion rates between 5–95%. The Wilson interval is more accurate for small samples or extreme proportions.