TL;DR
Bayesian A/B testing gives you "probability that B beats A" (e.g., 92% chance B is better) instead of just "significant or not." You can peek at results continuously without inflating error rates. Better for: low-traffic sites, business decision-making. Frequentist is simpler and more standard.
Bayesian vs Frequentist
| Aspect | Frequentist | Bayesian |
|---|---|---|
| Question answered | Is there a difference? | What's the probability B is better? |
| Output | p-value (reject null or not) | Probability B beats A (e.g., 87%) |
| Can peek? | No (inflates false positives) | Yes (posterior updates continuously) |
| Interpretation | Significant or not | Probability + expected loss |
| Sample size | Fixed upfront | Can be adaptive |
How to Interpret Bayesian Results
Bayesian output is more intuitive:
Frequentist: "p = 0.03"
Translation: If there's no real difference, there's a 3% chance of seeing this result. (Confusing!)
Bayesian: "92% probability B is better"
Translation: There's a 92% chance variant B actually performs better. (Clear!)
When to Use Bayesian
Good Use Cases:
- Low-traffic sites (can't wait months)
- Need to peek at results
- Business decision-making (want probability)
- Adaptive sample sizes
Stick with Frequentist:
- High-traffic sites (can wait)
- Want industry standard
- Team unfamiliar with Bayesian
- Regulatory requirements (FDA, etc.)
Choose Your Method
ExperimentHQ uses frequentist methods by default (industry standard). For Bayesian, consider GrowthBook or Statsig.