Data
Bayesian vs Frequentist A/B Testing: Which Should You Use in 2026?
Five years ago every A/B testing tool was frequentist. Today most major platforms — VWO, Statsig, Optimizely’s newer engines — default to Bayesian. Here’s why, and when the older approach still wins.
Frequentist in 30 Seconds
Asks: “If there’s no real difference, how unlikely is the result I observed?” Output: a p-value. Requires a fixed sample size decided in advance. Use our Statistical Significance Calculator.
Bayesian in 30 Seconds
Asks: “Given the data so far, what’s the probability B is better than A — and by how much?” Output: a probability and a credible interval. Allows continuous monitoring. Use our Bayesian A/B Test Calculator.
The Real-World Differences
| Aspect | Frequentist | Bayesian |
|---|---|---|
| Output | p-value | Probability B > A |
| Peeking | Inflates false positives | Safe (with proper priors) |
| Interpretation | Counterintuitive | Direct |
| Sample size | Fixed in advance | Flexible |
When Bayesian Wins
- You want stakeholder-friendly outputs.
- You can’t pre-commit to a sample size.
- You have informative prior data from previous tests.
When Frequentist Wins
- Regulatory or scientific contexts requiring p-values.
- You need replication-friendly methods reviewers expect.
- You don’t trust your prior assumptions.
FAQs
Will I get different winners? Usually no — both converge on the same answer with enough data. They differ in how they handle uncertainty along the way.
Which is “more correct”? Neither — they answer different questions.