Bayesian

A statistical approach based on using probability to model uncertainty around a model's parameters. Unlike the frequentist approach, it incorporates both observed data and, when available, prior information (called priors) to update the probability of a hypothesis as new data is collected.

In a CRO / A/B testing context:

The Bayesian approach helps answer concrete questions such as:

“What is the probability that variation B is better than variation A?”

Rather than rejecting or not rejecting a null hypothesis, it provides a direct, actionable probability, much more intuitive for business teams.

Benefits for CRO:

• Delivers continuously actionable results, without waiting for a predefined sample size,
• Adapts dynamically as the test evolves,
• Provides clear probabilistic estimates: for example, “there's a 92% chance that variation B is better than A”,
• Facilitates fast, progressive decision-making,
• Less exposed to biases related to early stopping (peeking),
• Compatible with advanced personalization contexts or multi-variant tests.

Tools that use the Bayesian approach:

• Optimizely (Stats Engine)
• VWO (Bayesian Engine)
• AB Tasty (Bayesian option)
• Convert.com, Google Ads (Experiments), etc.

Points to watch out for:

• The choice and interpretation of priors can influence results if poorly calibrated,
• Less familiar for purely data/scientific teams accustomed to traditional tests.