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A/B Test Calculator

How many visitors/conversions do you need for a statistically significant A/B test result? Calculates sample size per variant and estimated duration based on current rate + expected uplift + confidence level.

Current conversion rate (%)What your control version converts today.Expected uplift (%)How much better the variant needs to be for you to care.

ConfidenceRisk of false positive.Statistical powerChance of detecting a real uplift.Visitors/day (total)Total to be split between A and B.

Visitors per variant

45.377

Both groups combined: 90.754. Each variant needs to generate ~1.589 conversions for the test to have a statistical basis.

Estimated duration

~13 weeks

With 1.000 visitors/day, this is the time needed to reach sample size. Consider running at least 1 full weekly cycle (weekends behave differently).

Test parameters

Control rate (A)3,50%

Target rate (B)3,85%

Relative uplift+10,0%

Min. detectable uplift (MDE)±0,34%

Confidence95%

Power80%

Watch out — long test: >60 days usually turns into noise (seasonal, product or audience changes). Consider increasing traffic, relaxing the expected uplift or lowering confidence to 90%.

How to read this: You need 45.377 visitors in each variant (90.754 total) to have 95% confidence that an uplift of at least 10% is real, with 80% power to detect it if it exists. Ending early = high risk of a false winner.

Before testing — what has your competitor already validated?

Running an A/B test costs visitors and time. Batedor shows you what type of campaign your competitors have been running consistently — a signal that something works. Start testing what the market has already proven, not in the dark. 14-day free trial.

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