Bayesian A/B Testing Tool

Every day, businesses run A/B tests hoping to improve conversion rates. But most A/B testing tools only give you statistical significance - leaving you wondering: 'Should I actually ship this change?' Traditional approaches struggle with translating p-values into business impact, planning experiments feels like guesswork, and decisions get delayed by analysis paralysis. Our Bayesian A/B Testing Tool flips this script. It's a production-ready web application that translates statistical results into clear business decisions with financial impact. Beyond just significance testing, it provides expected revenue gain, downside risk quantification, and cost-of-delay calculations to make shipping decisions crystal clear. The experience is designed for business stakeholders: start with experiment planning where you input traffic, conversion rates, and expected uplift. Get instant assurance analysis showing exactly how long to run your test and how much traffic you need for conclusive results. Then, when you have test data, input your results and receive comprehensive analysis with statistical evidence, business economics, and clear decision guidance. Under the hood, the tool uses Bayesian statistical methods with Beta-Binomial models and Monte Carlo sampling for interpretable results. It's a client-side TypeScript SPA — the statistics engine runs entirely in the browser (ported from the original Python/NumPy implementation to TypeScript backed by @stdlib), so no test data ever leaves the user's machine. The Analyze flow recomputes live as you type; the Plan flow uses an explicit Calculate button because its assurance search is heavy compute. Deployed as a static SPA on Cloudflare Pages. The result: faster decisions, better resource allocation, and real revenue growth through experimentation that ties statistics to business outcomes.