Free online A/B test calculator. Рассчитать statistical significance, p-value, confidence intervals, and required sample size for your split tests. Optimize conversion rates with data-driven decisions. Pure client-side, no ads.
A/B testing (also known as split testing) is a method of comparing two versions of a webpage, app, or marketing asset against each other to determine which one performs better. This calculator helps you determine whether your test results are statistically significant, calculate confidence intervals, and estimate the sample size needed for reliable results. Все calculations happen entirely in your browser — no data is ever sent to a server. Use this tool to make data-driven decisions and optimize your conversion rates with confidence.
A/B testing is a method of comparing two versions of a webpage, email, or app against each other to determine which one performs better. Version A is the control (original), and version B is the variant with one changed element. By randomly splitting traffic between the two versions and measuring conversions, you can determine whether the change had a statistically significant impact.
Statistical significance means that the observed difference between your control and variant is unlikely to have occurred by random chance. Typically, a p-value less than 0.05 (at 95% confidence level) is considered statistically significant. This means there is less than a 5% probability that the difference is due to chance alone.
Run your A/B test for at least one full business cycle (typically 1-2 weeks) to account for daily and weekly variations. Use our sample size calculator to determine the minimum number of visitors needed per variant. Do not stop the test early just because you see a significant result — this leads to false positives.
Conversion rates vary widely by industry and traffic source. E-commerce averages 2-3%, B2B SaaS 5-10%, and finance/insurance can be higher. The most important benchmark is your own historical performance. Even a small improvement (like 0.5% relative lift) can have a significant revenue impact at scale.
Test Significance analyzes data you have already collected from a running or completed A/B test to determine if the difference between variants is statistically significant. Sample Size Calculator helps you plan a new experiment by estimating how many visitors you need per variant to detect a meaningful difference with your desired confidence level and statistical power.
95% confidence is the standard for most A/B tests and is recommended for most use cases. 90% confidence is acceptable for exploratory tests where you want faster results. 99% confidence is used for high-stakes decisions where you need near certainty. The higher the confidence level, the more visitors you need to reach significance.
Statistical power (typically 80% or 90%) is the probability that your test will detect a true effect when one exists. A test with low power might miss a real improvement, leading to a false negative. Higher power means you are more likely to detect meaningful differences, but it also requires a larger sample size.
Yes, but with caution. Running multiple simultaneous tests on the same page or user flow can lead to interaction effects where the results of one test influence another. To avoid this, ensure your tests target different, non-overlapping user segments, or use a multi-variate testing framework that accounts for interactions between variables.