What is the minimum detectable effect (MDE)?

The MDE is the smallest relative improvement in conversion rate you want to reliably detect. For example, an MDE of 10% on a 5% baseline means you want to detect a lift to at least 5.5%. A smaller MDE requires a larger sample because the signal is weaker.

What confidence level should I use?

95% is the industry standard. Use 90% when speed matters more than certainty (low-stakes UI changes), and 99% when the cost of a wrong decision is high (pricing, checkout flow).

What is statistical power?

Power (1 − β) is the probability of detecting a real effect when one exists. 80% power means there is a 20% chance of a false negative — missing a real improvement. 90% power cuts that risk to 10% but requires a larger sample.

Does this calculator support more than two variants?

It calculates sample size per variant for a two-variant (A/B) test. For multi-variant tests (A/B/C/D), multiply the per-variant number by the number of variants to get total traffic needed, then adjust for multiple comparisons (Bonferroni correction).

Why is the estimated days to completion so high?

Large sample sizes result from small MDEs or high confidence/power settings. Consider raising the MDE threshold, lowering the confidence level, or splitting traffic 50/50 rather than an uneven allocation.

Should I stop the test as soon as significance is reached?

No — running until the planned sample size is complete is important. Stopping early when significance is first reached inflates the false positive rate (peeking problem). Commit to the sample size before the test starts.

A/B Test Sample Size Calculator

Enter your baseline conversion rate, minimum detectable effect, and desired statistical power to find out how many visitors each variant needs.

Test Parameters

Baseline Conversion Rate

Your current conversion rate before the test.

Minimum Detectable Effect (MDE)

Smallest relative lift you want to reliably detect.

Confidence Level (1 − α)

Statistical Power (1 − β)

Daily Visitors to This Page (optional)

Used to estimate days to significance.

Fill in your test parameters,
then click Calculate Sample Size.

Summary

This calculator uses the standard two-proportion power analysis formula to determine the minimum sample size per variant for an A/B test. Enter your current (baseline) conversion rate, the smallest lift you care about detecting, your desired confidence level, and statistical power — and get the required sample size per variant plus an estimated time to significance based on your daily traffic.

How it works

Enter your current (baseline) conversion rate as a percentage.
Set the minimum detectable effect (MDE) — the smallest relative lift you want to be able to detect.
Choose a confidence level (1 − α): 90%, 95%, or 99%.
Choose statistical power (1 − β): 80%, 85%, or 90%.
Click "Calculate" to get the required sample size per variant and estimated days to significance.

Use cases

Plan a landing page redesign test and know when you will have enough data to call it.
Set a realistic timeline for a pricing page experiment before pitching it to stakeholders.
Avoid ending tests early by knowing exactly how many visitors each variant must reach.
Compare tradeoffs: a larger MDE requires fewer visitors, so you can ship faster with less certainty.
Size email subject line tests against your list size to check if the test is feasible.
Decide whether a low-traffic page can realistically support an A/B test at all.

Frequently Asked Questions

Last updated: 2026-06-19 · Reviewed by Nham Vu