A/B Test Sample Size Calculator
Built with ❤️ for testing, optimization, UX, CRO, and design teams.
Minimum improvement in conversion rate you want to detect (%)
Estimated existing conversion rate (%)
Number of variations/combinations (including control)
Traditional A/B testing
Using Frequentist Statistics
Using Bayesian Statistics
Why does VWO run on Bayesian Statistics?
Intuitive Test Reports
We realized our non-statistical users frequently (and wrongly) interpreted the frequentist p-value as a Bayesian posterior probability (the probability that variation is better than control). So we built the industry's first Bayesian statistical engine that gives you an easily understandable result. An intuitive result ensures that you don't make a mistake while A/B testing revenue or other critical KPIs.
No Sample Sizing Required
VWO SmartStats relies on Bayesian inference which unlike a frequentist approach doesn’t need a minimum sample size. This allows you to run A/B tests on parts of your website or apps that might not get a lot of traffic to improve them. However, getting more traffic on your tests allows VWO to determine your conversion rates with more certainty allowing you to be more confident about your test results.
Actionable Results, Faster
VWO SmartStats was engineered keeping one key metric in mind: Speed. We have traded-off some accuracy for speed, not a lot, just a tiny bit, enough to get quicker results without impacting your bottom line. This frees up your time enabling you to test more. Also, on the off chance that you would want to be absolutely and completely sure, we calculate the maximum potential loss you'd be taking, and you can decide if the loss value matches your risk appetite.
Unlock the power of Bayesian statistics for your experimentation program
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Get delivered to your inbox VWO's ebook on 'Introduction to Bayesian A/B Testing'
- Getting started with Bayesian
- Frequentist v/s Bayesian
- Random Variables, Distributions and their Implications
- Bayesian Learning and Inference
- Case Study