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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Step-by-step guide to get accurate results with A/B Test Calculator
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 analysis tool that determines whether the results from your website experiments are statistically significant. It calculates p-values, confidence intervals, and effect sizes to help you make data-driven decisions about implementing changes. Our free A/B testing calculator supports both significance testing for completed experiments and sample size planning for future tests.
Statistical significance testing is crucial for avoiding false positives in A/B testing. Without proper statistical analysis, you might implement changes based on random fluctuations rather than real improvements. Our A/B test significance calculator prevents costly mistakes by ensuring your test results are statistically valid. Studies show that companies using rigorous A/B testing see 20-25% higher conversion rates compared to those making decisions based on intuition alone.
For significance testing: Enter your control and variant visitor counts plus conversion numbers. The calculator instantly shows statistical significance, p-values, and confidence intervals. For sample size planning: Input your baseline conversion rate and minimum detectable effect to determine required visitors. The tool provides duration estimates based on your daily traffic, helping you plan experiment timelines effectively.
Statistical significance indicates that your test results are unlikely to be due to random chance. A p-value of 0.05 (95% confidence) means there's only a 5% probability that the observed difference occurred by chance. Industry standard is 95% confidence for most A/B tests, though some companies use 99% for critical business decisions.
Run your test until you reach the calculated sample size AND for at least one full business cycle (usually 1-2 weeks minimum). For e-commerce, this captures weekly shopping patterns. For B2B SaaS, consider monthly cycles. Never stop early just because results look good - this leads to false positives 60% of the time.
Minimum sample size depends on your baseline conversion rate and desired effect size. Generally, you need at least 100-150 conversions per variant for reliable results. For 2% baseline conversion rate detecting 20% improvement, you need approximately 4,000 visitors per variant. Use our calculator for precise requirements.
Peeking significantly increases false positive rates. Studies show that checking results daily and stopping when 'significant' leads to wrong decisions 25-40% of the time. Use sequential testing methods (Bayesian or group sequential designs) if you need continuous monitoring, or set fixed calendar dates for result reviews.
Statistical significance means the result is unlikely due to chance (p < 0.05). Practical significance means the result is large enough to matter for your business. A 0.01% improvement might be statistically significant with enough traffic but not worth implementing. For most businesses, minimum detectable effect should be 10-20% improvement to justify implementation costs.
Conversion rates vary significantly by industry: E-commerce (2-3%), SaaS landing pages (2-5%), Lead generation (2-4%), B2B software trials (1-3%). Focus on improving your own baseline rather than comparing to others. A 10-20% relative improvement is considered a significant win in most A/B testing programs.
Limit concurrent tests to avoid interaction effects and traffic dilution. Most companies run 1-3 tests simultaneously on different page elements or user segments. Running too many tests reduces statistical power and makes it harder to detect significant differences. Prioritize tests based on potential impact and traffic allocation.
You need sufficient traffic to reach statistical significance within reasonable timeframes. For websites with <1,000 weekly visitors, focus on qualitative research first. Websites with 1,000-10,000 weekly visitors can run meaningful tests on high-impact elements. 10,000+ weekly visitors enable comprehensive testing programs with multiple concurrent experiments.