Bayesian Testing is an alternative to traditional (frequentist) . Instead of calculating p-values, it directly answers: "What's the probability that variant B beats variant A?"
Bayesian vs Frequentist Testing
Frequentist (traditional) approach:
- Calculates
- Requires fixed sample size
- Binary outcome: significant or not
- Cannot peek at results without penalty
Bayesian approach:
- Calculates probability of winning
- Updates continuously as data arrives
- Provides probability distributions
- Allows checking results anytime
How Bayesian Testing Works
Bayesian testing starts with a prior belief (often neutral) and updates it as data accumulates. The result is a probability distribution showing how likely each is.
Output example:
- "92% probability Variant B is better"
- "Expected lift: 5-15%"
- "Risk of choosing B if it's worse: 2%"
Advantages of Bayesian Testing
Intuitive results: "85% chance B wins" is easier to understand than "p = 0.03."
Early stopping allowed: Check results anytime without inflating false positive rates.
Risk quantification: Know the probability and magnitude of potential loss.
Works with small samples: Provides useful estimates even with limited data (though with wider uncertainty).
Limitations
Prior selection: Results can be sensitive to prior assumptions, though this effect diminishes with more data.
Computational complexity: More complex calculations than frequentist methods.
Less familiar: Many stakeholders understand p-values but not probability distributions.
When to Use Bayesian Testing
Good for:
- Continuous monitoring
- Business decisions with clear risk tradeoffs
- Teams comfortable with probabilistic thinking
Consider frequentist when:
- Regulatory requirements demand traditional statistics
- Team prefers familiar methodology
- Simple pass/fail decisions needed
