Calculate conversion rates with statistical significance testing, confidence intervals, and sample size requirements for A/B tests.
Use completed outcomes and their eligible opportunities to calculate an observed conversion rate. For an experiment, enter control and variant counts to compare rates, view statistical significance and confidence intervals, or estimate the sample needed to detect a chosen change. The common mistake is treating a significant result as the size or certainty of the business benefit. Significance addresses sampling evidence; the observed difference and its interval describe magnitude and uncertainty.
A concrete input and expected output from the current implementation.
Input
Eligible visits: 100 Conversions: 20
Expected output
Conversion rate: 20%
The conversion rate is 20 conversions divided by 100 eligible visits, multiplied by 100. That calculation gives 20%.
Each conversion rate is conversions divided by eligible opportunities, multiplied by 100. When control and variant counts are entered, their rates form the basis for the A/B comparison. A change from 10% to 12% is 2 percentage points, or 20% relative to the control. Relative lift and percentage-point change are not interchangeable.
The significance analysis uses conversion counts and total observations, not percentages alone, because uncertainty depends on sample size. Confidence intervals accompany the estimated rates or difference. Sample-size planning reverses the question: an assumed baseline rate, detectable change, and selected statistical settings determine the estimated observations needed for the planned A/B comparison.
Percentage-point change subtracts one rate from the other. Relative lift divides that difference by the baseline rate. For example, 10% to 12% is a 2 percentage-point increase but a 20% relative lift. Always identify which measure you mean, especially when entering a target effect for sample-size planning.
No. Significance addresses whether the observed difference would be unusual under the comparison's null model; it does not establish tracking quality, practical value, or freedom from bias. Review interval width, effect size, experiment design, guardrail metrics, and implementation cost. A small but statistically clear lift can still be commercially unimportant.
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