# Bayesian A/B Testing Calculator MCP for AI Agents MCP > Bayesian A/B Testing Calculator uses advanced statistical methods to evaluate website variant performance. Stop relying on simple p-values; this MCP quantifies conversion probability and expected loss with Bayesian inference, telling you exactly how confident you should be in a winner. ## Overview - **Category:** analytics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/mcp - **Tags:** bayesian, ab-testing, conversion-rate, statistical-inference, data-analysis ## Description When running A/B tests, simply checking the p-value doesn't give you the full picture. This MCP provides a powerful statistical engine that moves beyond basic significance testing. It uses the Beta-Bernoulli relationship to calculate the actual probability of one variant beating another. Instead of just flagging a difference, your agent tells you how much risk you take by making a wrong decision using tools like `calculate_expected_loss`. You can also determine exactly what uplift Variant B provides over Variant A with `calculate_expected_uplift`, giving you clear numbers for product prioritization and resource allocation. Once the math is done, the MCP helps guide your next steps through `evaluate_decision_recommendation` or confirm confidence levels using `calculate_superiority_probability`. By connecting this to Vinkius, you give your AI client instant access to sophisticated analytics that most internal tools just can't match. ## Tools ### evaluate_decision_recommendation Gives an actionable recommendation for a winner or loser based on your specific confidence threshold percentage. ### calculate_superiority_probability Calculates the precise probability that one variant is better than another, moving beyond traditional statistical tests. ### calculate_expected_loss Determines the expected loss in revenue or conversions if you choose the wrong variant for your product. ### calculate_expected_uplift Calculates how much better one variant is predicted to perform compared to a baseline version. ## Prompt Examples **Prompt:** ``` We tested a new checkout flow (B) vs old one (A). A had 80 conversions / 2000 visitors; B had 110 / 2000. What's the chance B is actually better? ``` **Response:** ``` **Superiority Probability Calculation** The probability that Variant B outperforms Variant A is approximately **96.3%**. This means we can be very confident in this result. * **Next Step:** Calculate expected uplift to confirm value. ``` **Prompt:** ``` If I choose the current flow (A) over the new one (B), what's the estimated loss? A: 50/1000, B: 60/1000. ``` **Response:** ``` **Expected Loss Analysis** Choosing Variant A results in an expected loss of **0.4%** compared to Variant B. This quantifies the risk for leadership: a decision against the better variant costs us measurable conversions. ``` **Prompt:** ``` We need a final recommendation for A (20/200) and B (35/200). Our threshold is 98% confidence. ``` **Response:** ``` **Decision Recommendation** Based on the 98% confidence threshold, the decision is **RECOMMENDED: Variant B**. The probability of superiority for B meets your minimum required certainty level. Implement this change immediately. ``` ## Capabilities ### Determine Winner Confidence Calculates the precise probability that one variant significantly outperforms another. ### Quantify Decision Risk Measures the expected loss associated with choosing either variant before testing is complete. ### Estimate Performance Gains Projects the anticipated uplift in conversion rate for one variant over a baseline. ### Generate Actionable Next Steps Provides clear, data-driven recommendations based on your required confidence threshold. ## Use Cases ### Landing Page Optimization Failure A CRO Manager runs two versions (A and B) of a sign-up page. They ask their agent to calculate the superiority probability, confirming that B has a 98% chance of being better than A, allowing them to greenlight the rollout immediately. ### Feature Rollout Risk Assessment A Product Analyst is debating between two pricing models. They use the expected loss calculator to determine that choosing the wrong model carries a $50k risk, forcing the team to prioritize testing high-impact changes first. ### Identifying Minimum Viable Changes A Marketing Director needs proof that a small copy change is worth the effort. They use the expected uplift calculation and see a projected 4% gain, proving the investment will pay off. ## Benefits - Moves beyond unreliable p-values. Instead of just knowing if a difference exists, you know the *probability* of that difference being real. - Quantify risk immediately. Use `calculate_expected_loss` to see the potential cost of making a bad product decision before you launch anything. - Pinpoint concrete gains with `calculate_expected_uplift`. You get a single, clear number showing how much better one variant is predicted to be. - Get direct guidance. The MCP uses `evaluate_decision_recommendation` to tell you what to do next, based on the confidence level your business requires. - Saves days of manual analysis. Instead of running complex scripts in R or Python, your agent handles the entire statistical pipeline instantly. ## How It Works The bottom line is that it converts raw traffic logs and conversion counts directly into actionable business probabilities, eliminating guesswork from product decisions. 1. Feed the MCP raw A/B test performance metrics—specifically conversion counts and total visitors for both variants. 2. The engine processes this data using Bayesian inference to generate probability distributions, moving past simple statistical significance. 3. Your AI agent synthesizes these results into clear numbers, telling you the likelihood of superiority or the expected financial loss if you choose incorrectly. ## Frequently Asked Questions **How does the Bayesian A/B Testing Calculator improve on standard statistical tests?** It moves beyond simple p-values by calculating a true probability distribution. Instead of just saying 'there is a difference,' it tells you how confident your team should be in that finding, providing much clearer direction. **Can I use this MCP to calculate the potential financial risk of poor A/B test results?** Yes. You can run the expected loss calculation to quantify exactly what you stand to lose if your team makes a decision based on incomplete or misleading data. **Do I need coding knowledge to use the Bayesian A/B Testing Calculator MCP?** No. Your AI agent handles all the complex statistical math behind the scenes. You just provide the raw conversion counts and visitor numbers, and it returns clear percentages. **What if I have more than two variants to test? Does the calculator handle that?** The MCP is designed for comparing pairs of variants (A vs. B). You can run multiple comparisons sequentially to build a comprehensive picture of performance across all versions. **How do I know if my results are 'good enough' to launch with the Bayesian A/B Testing Calculator?** The tool provides an explicit decision recommendation. You set your confidence threshold, and it tells you precisely whether the data meets that business standard for a go-ahead.