Bayesian Statistics

Bayesian Statistics is a statistical approach that treats probability as a degree of belief and continuously updates the probability of a hypothesis being true as new data is collected during an A/B test.

Meaning & Context

Unlike frequentist methods, Bayesian approaches incorporate prior knowledge or beliefs into the analysis and express results as probability distributions rather than binary significant/not-significant outcomes. Bayesian A/B testing provides statements like 'the probability that Variant B is better than Control is 94%' and allows you to stop tests early or peek at results without inflating error rates. This approach uses credible intervals instead of confidence intervals and calculates the expected loss of choosing each variation.

Why It Matters

Bayesian methods offer more intuitive interpretations of test results, making it easier for stakeholders to understand the probability of success and potential risk. They're particularly valuable for businesses that need to make faster decisions, can't wait for fixed sample sizes, or want to incorporate domain expertise into the analysis. However, Bayesian approaches require careful selection of priors and more complex calculations than traditional frequentist methods.

Example

Your Bayesian A/B test shows there's an 87% probability that the new checkout flow is better than the current one, with an expected lift of 6-12%. Even though this hasn't reached 95% certainty, you decide to implement it because the potential upside outweighs the minimal expected loss.