The upcoming GrowthBook 3.0 release introduces significant enhancements to its Bayesian engine, allowing users to specify their own priors, integrate CUPED for variance reduction, and improve estimation accuracy in small sample sizes. These changes simplify the process by focusing on treatment effects rather than variation averages, making it easier for users to set priors and analyze experiments. While some existing experiment results may experience minor shifts, these are generally minimal and aim to enhance the power of the analysis engine. The new model facilitates improved computation of inferential statistics by employing techniques like the Delta method for variance and CUPED for faster experimentation, especially in handling small sample sizes more effectively. Although the previous model was not inaccurate, the new model offers a more streamlined setup and increased analytical power, making it an advantageous update for users.