“Statistics is the art of never having to say you are wrong…”
We may not be able to avoid being wrong altogether, but as A/B testing analysts we can do our best to control the probability of it happening. This calculator will help you determine the sample size needed to achieve sufficient statistical power. Start by selecting the type of your KPI, enter the relevant parameters, and we will do the rest.
Planning an A/B test? This is the right place to start. Already have data? Check our A/B test analysis calculator.
Use this calculator when your outcome is binary — for example, converted or not, signed up or not, or active on day 7 — and you are comparing two independent groups.
Whether you call it an A/B test sample size calculator, a split test sample size calculator, or a conversion rate sample size calculator, this is the right tool for planning your next experiment.
Common use cases include testing a new checkout flow, a pricing page, a signup form, or any change where the outcome you care about is a simple yes or no. If you already have results and need to check for statistical significance, use our A/B test analysis calculator instead.
The calculation assumes:
This A/B test sample size calculator estimates the sample size needed to detect a specified difference between two proportions (conversion rates) at a chosen significance level, power, and allocation ratio. You can choose whether the calculation uses a relative or an absolute effect:
The difference between the two proportions.
The difference relative to the baseline proportion.
The general two-proportion formula, for any allocation ratio r = n₂/n₁ and either a one- or two-sided test. r = 1 gives an even split, where p̄ reduces to the simple average of p₁ and p₂ — away from it, p̄ is the weighted average shown above, not the simple one. The critical value in the first term is z₁₋α for a one-sided test and z₁₋α/2 for a two-sided one; both use the pooled proportion under the null in that first term and each group's own variance under the alternative in the second.
This A/B test sample size calculator shows the number of visitors or users you need per variation and in total to reach your chosen statistical power for the selected minimum detectable effect (MDE).
If you run your test with this sample size and the true effect is at least as large as the MDE, your test has a probability of at least the selected power (for example, 80%) to detect a statistically significant result. Smaller effects can still reach statistical significance, but the chance of detecting them is lower than the specified power.
Tip: Stopping your A/B test before it reaches the required sample size lowers its power and makes it more likely to miss a real effect. If you also check results repeatedly and stop as soon as they look significant, you increase the risk of false positives. For reliable results, decide on the sample size before launching and run the test until you reach it.
Suppose your baseline conversion rate is 4.0% and you want to detect an increase to 4.5%.
The calculator requires approximately 25,551 users per group, or 51,102 in total. At 6,000 eligible users per day, this is approximately 9 days of traffic.
This sample size gives you an 80% probability of detecting a 0.5-percentage-point effect if that effect truly exists. Smaller effects may still be detected, but with lower probability.
Use this calculator when your outcome is continuous — for example, revenue per user, session duration, or time on page — and you are comparing two independent groups.
Whether you call it a two-sample t-test sample size calculator, an A/B test sample size calculator for continuous metrics, or a mean comparison sample size calculator, this is the right tool for planning your next experiment.
The calculation assumes:
The calculator estimates the sample size needed to detect a specified difference between two group means at a chosen significance level, power, and allocation ratio, using the t-distribution to account for sample-based variance estimation. You can choose whether the calculation uses relative vs. absolute effect:
The difference between the two group means, in the same units as the metric.
The difference relative to the baseline mean.
Everything the design depends on is in this one number. A difference of 5 on a standard deviation of 120 and a difference of 2 minutes on one of 48 minutes are the same experiment and need the same sample. If you know the effect only in your own units, this is where the standard deviation enters.
The calculator uses the noncentral t-distribution to compute the sample size. It finds the smallest sample size for which the power reaches the target value. The noncentrality parameter (λ) is how far the expected effect sits from the null, given the effect size and the two sample sizes — a larger λ means the effect is easier to detect, so power increases. Two-sided power is the probability of detecting an effect in either direction, with the significance level split between both tails. One-sided power places the whole significance level in one tail, which is why a one-sided test reaches the same power with a smaller sample.
The form collects one standard deviation, so both groups are planned at the same variance either way and only the degrees of freedom change. At n₁ = n₂ the Welch expression is exactly n₁ + n₂ − 2, so the two agree to the last digit on an even split; away from it Welch's df is smaller and the design is a little more conservative. Ticking the box uses the pooled figure.
This A/B test sample size calculator shows the number of visitors or users you need per variation and in total to reach your chosen statistical power for the selected minimum detectable effect (MDE).
If you run your test with this sample size and the true effect is at least as large as the MDE, your test has a probability of at least the selected power (for example, 80%) to detect a statistically significant result. Smaller effects can still reach statistical significance, but the chance of detecting them is lower than the specified power.
Tip: Stopping your A/B test before it reaches the required sample size lowers its power and makes it more likely to miss a real effect. If you also check results repeatedly and stop as soon as they look significant, you increase the risk of false positives. For reliable results, decide on the sample size before launching and run the test until you reach it.
Suppose your baseline average revenue per user is 50, with a standard deviation of 120, and you want to detect an increase to 55 — at 2,000 eligible users a day.
9,043 users per group, or 18,086 in total. At 2,000 eligible users per day, this is approximately 10 days of traffic. The standardised effect is d = 0.042, which is small — and that is what a standard deviation more than twice the mean does to an experiment.
This sample size gives you an 80% probability of detecting a 5-unit effect if that effect truly exists. Smaller effects may still be detected, but with lower probability.
Use this calculator when your outcome is a ratio metric — a value calculated as one quantity divided by another, measured per randomization unit — for example, revenue per user, clicks per impression, or items purchased per session.
The calculation assumes:
The calculator estimates the sample size needed to detect a specified difference between two group ratios at a chosen significance level, power, and allocation ratio, based on the baseline ratio and its standard deviation. If you don't already know these two values, the built-in helper calculator can derive them for you — either from uploaded raw data or from summary statistics (numerator mean, denominator mean, numerator variance, denominator variance, and covariance) using the delta method. You can choose whether the calculation uses relative vs. absolute effect:
The difference between the two group ratios, in the same units as the metric.
The difference relative to the baseline ratio.
This is what the helper calculator runs — R is the ratio, and the bracket is its per-unit variance. The first term is what you get pretending the denominator is constant; the third is what the denominator's variation costs; the middle one subtracts, and it is a refund: a user with more sessions who also has more revenue has a ratio that moves less than either quantity does.
The ordinary two-sample normal formula, with the ratio's own standard deviation in place of σ. Δ is the change in the ratio itself — a 2% lift in revenue per session, not a 2% lift in revenue.
This A/B test sample size calculator shows the number of visitors or users you need per variation and in total to reach your chosen statistical power for the selected minimum detectable effect (MDE).
If you run your test with this sample size and the true effect is at least as large as the MDE, your test has a probability of at least the selected power (for example, 80%) to detect a statistically significant result. Smaller effects can still reach statistical significance, but the chance of detecting them is lower than the specified power.
Tip: Stopping your A/B test before it reaches the required sample size lowers its power and makes it more likely to miss a real effect. If you also check results repeatedly and stop as soon as they look significant, you increase the risk of false positives. For reliable results, decide on the sample size before launching and run the test until you reach it.
Suppose your baseline revenue-per-session ratio is $25, with a standard deviation of 3, and you want to detect an increase to $27.50.
The calculator requires approximately 23 users per group, or 46 in total. At 10 eligible users per day, this is approximately 5 days of traffic.
This sample size gives you an 80% probability of detecting a $2.50 effect if that effect truly exists. Smaller effects may still be detected, but with lower probability.
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We size, run and read experiments where the metric, the sample size and the decision rule are agreed before anyone looks at the data. A/B Testing