Conversion & CROFree
A/B Test Calculator
Calculates how many visitors each variant of an A/B test needs for a statistically significant result and how long the test should run. Uses your current conversion rate, expected uplift, confidence and statistical power.
- No sign-up
- Runs in your browser
Both groups combined: 90.754. Each variant needs about 1.589 conversions for the test to have a statistical basis.
With 1.000 visitors per day, this is the time needed to reach the sample. Run at least one full week: weekends behave differently.
Test parameters
Long test: beyond 60 days, seasonal, product or audience changes tend to distort the result. Increase traffic, raise the expected uplift or lower 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. If you stop early, the risk of picking a false winner is high.
Before testing, see what your competitor has already validated
An A/B test costs visitors and time. Batedor shows which types of campaign your competitors keep running consistently, a sign that they work. Start by testing what the market has already approved. 14-day free trial.
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How to calculate A/B test sample size
The calculator takes your store's current conversion rate, the uplift you want to detect (the minimum detectable effect, or MDE) and the confidence level (usually 95%) to estimate how many visitors each variant needs before the result becomes statistically significant. The smaller the uplift you want to catch, the more traffic the test demands. From your daily traffic it also projects the estimated duration in days.
Example: your page converts at 2% and you want to detect a 10% relative lift (from 2% to 2.2%) at 95% confidence. The tool points to roughly 30,000 to 40,000 visitors per variant. If the store gets 2,000 visits/day split between the two versions, the test runs around 30 days to reach the full sample.
A common pitfall in Brazilian e-commerce is stopping the test early, on the first day B looks like it is winning. Wait for the full sample and run at least one or two whole weeks, covering weekdays and weekends, because buying behavior shifts a lot between Monday and Saturday and around dates like Black Friday.
FAQ
Frequently asked questions
What is an A/B test calculator?
It's a tool that calculates how many visitors and conversions you need per variant for an A/B test to reach statistical significance. From your current conversion rate, expected uplift and confidence level, it estimates the required sample size and test duration. Runs free in your browser.
How do I calculate the sample size for an A/B test?
Enter your current conversion rate (baseline), the minimum uplift you want to detect and a confidence level (e.g. 95%). The calculator returns the number of visitors and conversions needed per variant for a statistically significant result, so you don't have to run the statistical-power math by hand.
How long should an A/B test run?
Duration depends on the required sample size divided by your traffic volume. The tool estimates how long the test should run based on the calculated sample and your usual number of visitors, so you don't stop the test too early and draw a premature conclusion.
What is the difference between confidence level and expected uplift?
Confidence level (e.g. 95%) measures how likely the result isn't due to chance. Expected uplift is the minimum improvement you want to detect between variants (e.g. going from 2% to 2.4% conversion). The higher the confidence and the smaller the uplift, the larger the sample you need.
Is the A/B test calculator free?
Yes. Batedor's A/B test calculator is 100% free, runs right in your browser and needs no signup. Adjust the current rate, uplift and confidence level and instantly see the sample size per variant and the estimated test duration.
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