MCP For Claude Marketplace
Bayesian A/B Testing Calculator

Bayesian A/B Testing Calculator MCP Connector for Claude

A+

Quantify conversion probability, expected loss, and uplift using Bayesian inference.

4 tools Official Updated Jun 29, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a powerful statistical engine for evaluating A/B test results. Using the Beta-Bernoulli conjugate prior relationship, it allows you to move beyond simple p-values and understand the actual probability of one variant outperforming another. You can use tools like calculate_superiority_probability to determine how confident you are in a winner, calculate_expected_loss to quantify the risk of making a wrong decision, and evaluate_decision_recommendation to get actionable next steps based on your specific confidence threshold.

bayesianab-testingconversion-ratestatistical-inferencedata-analysis

4 tools expose this connector's capabilities to your AI agent.

evaluate_decision_recommendation

Evaluate the decision recommendation based on a threshold

calculate_superiority_probability

Calculate the probability that Variant B is better than Variant A

calculate_expected_loss

Calculate the expected loss for choosing either variant

calculate_expected_uplift

Calculate the expected uplift of Variant B over Variant 1

See how to talk to your AI agent using Bayesian A/B Testing Calculator.

Calculate the probability that Variant B is better than Variant A, where A has 100 conversions out of 1000 visitors and B has 130 conversions out of 1000 visitors.

The probability that Variant B is superior to Variant A is approximately 94.5%.

What is the expected loss if I choose Variant B, given A has 50/500 conversions and B has 60/500 conversions?

The expected loss for choosing Variant B is approximately 0.004 (or 0.4%).

Evaluate the decision recommendation for A: 20/200, B: 30/200 with a 95% threshold.

The decision is INCONCLUSIVE because the probability of superiority does not meet the 95% threshold.

It tells you the likelihood that Variant B's conversion rate is higher than Variant A's, based on your observed data.

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