A/B and Multivariant Testing Using Bayesian Algorithm

Abstract

A/B testing and multivariate testing are widely used methodologies for evaluating the effectiveness of different variations in online experiments. These methods provide a valuable means to optimize user experience, increase conversions, and enhance overall performance. Traditionally, frequentist statistical approaches have been employed to analyze the results of such experiments. However, Bayesian algorithms have gained attention in recent years due to their ability to handle small sample sizes, incorporate prior knowledge, and provide more robust results. This paper presents a comprehensive overview of A/B and multivariate testing methodologies, with a specific focus on utilizing Bayesian algorithms for analysis. We explore the key concepts and principles underlying A/B testing, including randomization, control groups, and statistical significance. Additionally, we delve into the principles of multivariate testing, which allows for evaluating multiple variations simultaneously and measuring their individual impact.