When to use Bayesian experiments: A beginner’s guide

Bayesian A/B testing is an alternative to traditional Frequentist methods, offering a more intuitive and flexible approach by assigning probabilities to hypotheses and incorporating prior knowledge. This statistical framework is particularly advantageous when dealing with small sample sizes, complex models, or situations requiring continuous result monitoring, as it updates beliefs with new data and allows for early test stopping. The Bayesian approach is user-friendly for non-technical stakeholders and provides clearer decision-making by quantifying risk. Platforms like Statsig facilitate the implementation of Bayesian experiments, providing tools for setting up, running, and analyzing results, making them accessible and easier to interpret. The ability to use historical data and expert knowledge enhances the accuracy of results, and Bayesian methods are well-suited for dynamic environments where conditions change rapidly.