Complex A/B Testing Framework for Conversion Rate Optimization

Build a comprehensive A/B testing statistical analysis framework that goes beyond basic hypothesis testing. Develop a Python script that handles multiple variants, accounts for sequential testing risks, implements Bayesian statistical methods, and provides advanced metrics like expected value of perfect information (EVPI). Include robust confidence interval calculations, multiple comparison corrections, and interactive visualization of test results with probabilistic decision support.