Complex A/B Testing Statistical Inference Framework

Build a comprehensive A/B testing analysis framework in Python that goes beyond basic significance testing. Implement advanced statistical techniques including Bayesian hypothesis testing, multi-armed bandit optimization, and sequential testing methods. The system should handle multiple variants, calculate precise effect sizes, and generate interactive visualizations showing statistical power, probability of superiority, and expected value of additional sampling. Include robust handling of sequential testing bias and multiple comparison problems.