# Bayesian A/B Testing Calculator MCP for AI Agents AI Agent Connect

> Bayesian A/B Testing Calculator lets you move past simple p-values to see the actual probability of one variant winning over another. It calculates conversion probabilities, expected losses, and uplift using Bayesian inference. This means you get a clearer picture of risk and confidence when deciding which product change to ship. It turns raw data into a definitive roadmap for your growth experiments.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_k0dC7IEUCVVW7fYVOUh3T85V4ei1IvThJnPQPt0d/ai-agent-connect
- **Tags:** bayesian, ab-testing, conversion-rate, statistical-inference, data-analysis

## Description

Most growth teams get stuck in p-value hell, where they have enough data to see a winner but not enough to satisfy a strict significance threshold. This leaves them guessing about whether to ship a feature or keep testing. This Connector fixes that by using Bayesian inference to show you the actual probability of one variant outperforming another. Instead of a binary yes or no, you get a clear picture of your confidence. It helps you understand the risk of making the wrong choice by quantifying the expected loss. If you're tired of manually crunching numbers or arguing over whether a result is real, this tool gives you the math to make a definitive call. You can connect it to your AI client through the Vinkius catalog to turn raw conversion numbers into a clear roadmap. It takes the guesswork out of data analysis by focusing on what actually matters for your bottom line. You can stop worrying about the nuances of frequentist statistics and start making decisions based on what is most likely to happen in the real world. Whether you are testing a new checkout flow or a simple button color, this tool provides the clarity needed to move fast without breaking things.

## Tools

### calculate_expected_loss
This calculates the potential downside you face by choosing one variant over the other. It lets you see the actual risk in terms of conversion units rather than abstract math.

### calculate_expected_uplift
This gives you a concrete number on how much better Variant B is expected to perform compared to Variant A. It's the best way to project the real-world impact of a successful test.

### evaluate_decision_recommendation
This tool takes your data and a specific confidence threshold to give you a clear recommendation. It tells you exactly whether to ship the winner or keep testing.

### calculate_superiority_probability
This tool tells you the actual percentage chance that Variant B is outperforming Variant A. It helps you move past binary win/loss thinking to see the true confidence of your data.

## Prompt Examples

**Prompt:** 
```
A has 500 conversions out of 10,000 visitors. B has 750 conversions out of 10,000 visitors. What's the probability B is better?
```

**Response:** 
```
Based on the data provided, here is the breakdown for your test:

| Variant | Conversions | Visitors | Conversion Rate |
| :--- | :--- | :--- | :--- |
| Variant A | 500 | 10,000 | 5.0% |
| Variant B | 750 | 10,000 | 7.5% |

**Results:**
* **Win Probability:** There is a **98.2%** probability that Variant B is superior to Variant A.
* **Confidence:** This result is highly significant for a production rollout.

You can proceed with Variant B with high confidence.
```

**Prompt:** 
```
What's the expected loss if I pick B, given A has 2% conversion and B has 2.5%?
```

**Response:** 
```
If you choose Variant B, the expected loss is **0.005 (or 0.5%)**.

This means that even though Variant B is the predicted winner, there is a small statistical risk that it might underperform relative to your expectations. In most growth scenarios, a risk this low is considered acceptable for a rollout.
```

**Prompt:** 
```
Evaluate the decision for A (10/100) and B (15/100) with a 90% confidence threshold.
```

**Response:** 
```
I've evaluated the decision for your test results:

* **Variant A:** 10 conversions / 100 visitors
* **Variant B:** 15 conversions / 100 visitors
* **Confidence Threshold:** 90%

**Decision:** **INCONCLUSIVE**

The probability of superiority does not meet your 90% threshold yet. You should continue running the test to gather more data before making a final decision.
```

## Capabilities

### Calculate win probability
See the actual percentage chance that one variant beats another.

### Quantify risk of loss
Determine the potential downside of picking a losing variant.

### Estimate performance uplift
Get a clear number on how much better one version is expected to perform.

### Generate decision logic
Get a clear go or no-go recommendation based on your risk tolerance.

### Analyze conversion data
Turn raw visitor numbers into statistical confidence.

## Use Cases

### Landing Page Optimization
A marketer has two designs and wants to know the probability of one winning before the traffic dies out.

### Feature Flag Validation
A product manager needs to see the expected loss of rolling out a new button to 100% of users.

### Email Campaign Analysis
An analyst wants to see the expected uplift of a new subject line to justify a full rollout.

### Risk-Averse Product Launches
A team with a high risk-aversion threshold uses the tool to ensure they only ship when the win probability is over 99%.

## Benefits

- Stop guessing about test results by using calculate_superiority_probability to see the real chance of a winner.
- Minimize the risk of shipping a bad feature by using calculate_expected_loss to quantify the potential downside of your choices.
- Predict the real-world impact of a change using calculate_expected_uplift to see exactly how much your conversion rate will move.
- Get immediate, actionable advice by using evaluate_decision_recommendation to decide when to stop a test and start shipping.
- Move beyond p-values to a more intuitive Bayesian framework that aligns with how business leaders actually make decisions.

## How It Works

The bottom line is you get a data-backed go or no-go instead of a confusing p-value.

1. Feed your AI client the raw conversion data from your test variants.
2. The Connector runs Bayesian calculations to find probabilities and expected losses.
3. You get a clear recommendation on which variant to ship based on your risk threshold.

## Frequently Asked Questions

**What is the Bayesian A/B Testing Calculator?**
It is a tool that helps you understand the real probability of one variant winning over another in an A/B test, rather than just giving you a p-value.

**How does this help with conversion rates?**
It takes your conversion data and calculates the actual likelihood of success, helping you decide which version to ship with more confidence.

**Can I use it for low-traffic tests?**
Yes, Bayesian inference is often better for low-traffic scenarios because it provides a probability of winning rather than waiting for a high sample size.

**What is expected loss in A/B testing?**
Expected loss quantifies the risk of choosing a variant that might actually perform worse than the current winner, helping you balance risk and reward.

**Why use Bayesian instead of p-values?**
Bayesian methods are often more intuitive for business decisions because they tell you the probability of a result being true, which is easier to explain to stakeholders.

**How do I get a go or no-go recommendation?**
By providing your test data and a confidence threshold, the tool evaluates the results and tells you clearly whether the data supports a rollout.

**What does the superiority probability tell me?**
It tells you the likelihood that Variant B's conversion rate is higher than Variant A's, based on your observed data.

**How do I use the decision recommendation tool?**
Provide your conversion and visitor counts for both variants. You can also set a `confidenceThreshold` (e.g., 0.95) to define how much certainty you require before the tool recommends choosing Variant B.

**What is 'Expected Loss' in this context?**
Expected loss quantifies the potential downside risk. It represents the expected reduction in conversion rate if you choose a variant that is actually inferior to the other.