A/B Test Sample Size Calculator
Find the right sample size for your A/B test in seconds. Enter your baseline conversion rate, minimum detectable effect (MDE), and statistical significance level to know how many visitors you need per variation for reliable results.
A sample size calculator estimates the number of visitors or users required in each variation of your A/B test to detect a meaningful difference in performance.
It helps ensure that your test results are statistically valid and not due to random chance. By using an accurate sample size, you can confidently decide which version of your webpage, ad, or campaign performs better.
In short, it removes guesswork and ensures your testing results are backed by data.
Testing with too few visitors can produce false positives or misleading results. A proper sample size ensures your findings are statistically sound and repeatable.
Running tests with unnecessary traffic wastes ad spend. Calculating the right sample size helps you stop testing once you’ve reached reliable data.
Knowing the target sample size helps you estimate how long your test will take to reach significance, allowing better test scheduling and planning.
Your chosen confidence level (for example, 95%) and minimum detectable effect determine how sensitive your test is. The right balance ensures you detect real changes while avoiding false signals.
Accurate A/B testing means faster optimization, better-performing campaigns, and higher ROI across all marketing efforts.
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