A/B Test Sample Size Calculator

The most common mistake in e-commerce A/B testing is ending tests too early. A test that shows a 15% uplift after 500 visitors might show zero uplift after 5,000 visitors — the early result was just noise. This calculator tells you the exact number of visitors you need per variation to detect a real difference with statistical confidence, so you never make a decision based on incomplete data.

Sample size depends on three factors: your baseline conversion rate, the minimum detectable effect (the smallest improvement you care about), and your desired statistical power. This calculator handles the math and translates it into practical terms — how many days your test needs to run given your traffic volume, and what minimum improvement is realistic to detect at your traffic level.

If the required sample size translates to more than 4-6 weeks of testing, you have two options: increase your minimum detectable effect (look for bigger wins) or increase traffic to the tested page. Tests running longer than 6 weeks are vulnerable to seasonal effects and external confounders.

This is one of the core reasons Eevy AI uses genetic algorithms instead of traditional A/B testing. A standard A/B test compares 2-4 variations and needs weeks of data. Eevy AI tests hundreds of layout combinations simultaneously, using evolutionary selection to converge on winners faster. It achieves statistical rigor not through large per-variation samples but through the mathematical efficiency of genetic optimization across many small experiments.