Calculate statistical significance and plan sample sizes for your experiments. Get instant results with confidence intervals, p-values, and clear recommendations.
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Follow this step-by-step guide to get accurate results
Start with a clear hypothesis about what you expect to change and by how much. For example: 'Changing the CTA button color will increase conversions by 15%'
Pro Tip: Write down your hypothesis before running the test to avoid confirmation bias
Use our sample size calculator to determine how many visitors you need. Input your baseline conversion rate and minimum detectable effect.
Pro Tip: Plan for at least 2 weeks of testing to account for weekly patterns in user behavior
Split traffic evenly between variants and collect data. Avoid peeking at results until you reach statistical significance.
Pro Tip: Never stop a test early just because you like the results - this leads to false positives
Enter your test data into our significance calculator. Look at p-value, confidence intervals, and practical significance.
Pro Tip: A statistically significant result isn't always practically significant - consider the business impact
Implement the winning variant if results are both statistically and practically significant. Document learnings for future tests.
Pro Tip: Even 'failed' tests provide valuable insights about user behavior and preferences
An A/B test calculator is a statistical tool that helps you determine whether the results of your split tests are statistically significant. It analyzes your test data to calculate p-values, confidence intervals, and provides clear recommendations on whether to implement changes based on your experiment results.
A/B testing is crucial for data-driven product decisions, but without proper statistical analysis, you might make wrong choices based on random fluctuations. This calculator ensures your decisions are backed by statistical rigor, preventing costly mistakes and helping you identify real improvements to your product or website.
Enter your test data including the number of visitors and conversions for both your control (original) and variant (new version). The calculator will automatically compute statistical significance, p-values, and provide clear recommendations. For planning future tests, use the sample size calculator to determine how many visitors you need.
Statistical significance indicates that your test results are unlikely to be due to random chance. A p-value of 0.05 means there's only a 5% probability that the observed difference occurred by chance.
Run your test until you reach the calculated sample size AND for at least one full business cycle (usually 1-2 weeks). This ensures you capture different user behaviors and seasonal patterns.
It depends on your baseline conversion rate and the minimum effect you want to detect. Generally, you need at least 100 conversions per variant, but our calculator will give you the exact number for your situation.
It's not recommended. Peeking and stopping tests early when you see positive results leads to false positives. Use sequential testing methods if you need to monitor results continuously.
Statistical significance means the result is unlikely due to chance. Practical significance means the result is large enough to matter for your business. A 0.01% improvement might be statistically significant but not worth implementing.