Email Open Rate Calculator
Work out email open rate from unique opens and emails delivered.
Compare two variants and see whether the difference is conclusive.
Variant A is the control, variant B the challenger. Use the same date range for both.
Relative uplift, B vs A
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The A/B Test Significance Calculator compares two variants by visitors and conversions, working out both conversion rates, the relative uplift and the confidence level from a two-tailed z-test, then says plainly whether the result is conclusive.
The hardest part of an A/B test is not running it — it is resisting the urge to call a winner too early. A variant three points ahead after 200 visitors usually means nothing at all, and shipping on that basis is how teams end up chasing noise for months.
Enter visitors and conversions for both variants and the calculator works out each conversion rate, the relative uplift, the absolute difference in percentage points, and the confidence level from a two-tailed z-test of two proportions. The verdict underneath says plainly whether the result clears the usual 95% threshold, is leaning that way, or could still easily be chance — with a warning when the sample is too small to trust either way.
A two-tailed z-test for two proportions, not a rule of thumb.
Says whether the result is conclusive, leaning, or still noise.
Flags when there are too few conversions to trust the answer either way.
No sign-up, no data sent anywhere.
Visitors and conversions for the control.
Visitors and conversions for the challenger.
Both conversion rates, the relative uplift and the absolute difference.
Confidence level plus a plain-English read on whether the result is conclusive.
This calculator uses a two-tailed z-test for two proportions: z = (rate B − rate A) ÷ the square root of pooled rate × (1 − pooled rate) × (1 ÷ visitors A + 1 ÷ visitors B), where the pooled rate is total conversions divided by total visitors. The confidence level is 100% minus the resulting p-value.
95% is the conventional threshold, meaning roughly a one-in-twenty chance of seeing a difference this large if the two variants were really identical. It is a convention, not a law of nature — decide the threshold before you look at the result, not after.
That the difference is unlikely to be chance alone. It does not tell you the uplift will hold at the same size, and it says nothing about whether the change is worth making commercially — a statistically significant 0.2-point gain may not pay for the work.
Because checking repeatedly and stopping at the first significant moment inflates false positives badly. Set the sample size and duration in advance, run full weeks to cover weekday and weekend behaviour, and read the result at the end.
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