Bayesian A/B Testing
An alternative to frequentist A/B testing that uses probability distributions to express uncertainty about treatment effects, allowing continuous monitoring without inflating error rates.
Bayesian A/B testing offers a fundamentally different approach to experimentation. Instead of asking "Is the difference significant?" (frequentist), Bayesian testing asks "What's the probability that the variation is better, and by how much?"
Bayesian vs. Frequentist: The Key Difference
- Frequentist: "There's a 5% chance we'd see this data if there were no difference" (p-value)
- Bayesian: "There's a 94% probability the variation is better, with an expected lift of 3-7%" (posterior probability)
The Bayesian answer is more intuitive and directly useful for business decisions.
Advantages of Bayesian Testing
- Continuous monitoring: You can check results anytime without inflating error rates
- Intuitive interpretation: "There's an 87% probability B is better" is easier to act on than "p = 0.04"
- Incorporates prior knowledge: You can factor in what you already know about similar tests
- Better for small samples: Produces useful (if uncertain) estimates even with limited data
When to Use Bayesian vs. Frequentist
Bayesian: When you need to monitor continuously, when stakeholders want probability statements, when you have informative priors from similar tests
Frequentist: When you need regulatory-grade rigor, when your organization is accustomed to the framework, when you have a fixed-sample design
Practical Advice
Most modern testing platforms (VWO, Dynamic Yield) now offer Bayesian analysis. The choice of framework matters less than the rigor of your test design. A well-designed frequentist test beats a sloppy Bayesian test every time.