Complex A/B Testing Statistical Significance Framework

Create a comprehensive Python framework for advanced A/B testing analysis that goes beyond traditional statistical methods. Develop a solution that implements multiple significance testing approaches including Bayesian inference, multi-armed bandit algorithms, and sequential testing methodologies. The framework should handle complex scenarios with multiple variants, provide robust confidence interval calculations, and generate interactive visualizations of test results.