Explain CLT

Inspiration

The Central Limit Theorem is a very robust and useful theorem in statistics. Like many others, we had a hard time understanding and appreciating it when we learned it for the first time. We felt that it would be nice to have a clear and easy demonstration. We realized that WebMCP can integrate AI, scripts, and data and thereby demonstrate this and many other theorems and theories.

What it does

This is a WebMCP app hosted at our website, acctey.org. It can demonstrate the Central Limit Theorem by allowing us to take dummy population data of different distributions, repeatedly draw a random sample and compute its mean, and check the distribution and mean of means of samples. It remarkably makes it clear that the sample means are normally distributed and their mean tends towards the population mean.

How we built it

In Google Chrome, we navigated to chrome://flags/#enable-webmcp-testing and set the flag to Enabled. ... We coded index.html, webmcp.js, and index.php files by taking some help from ChatGPT, Copilot, Gemini, and Claude. After checking that they did work locally, we posted them to our website at acctey.org.

Challenges we ran into

We had some difficulty in understanding MCP and WebMCP. However, the excellent resource documents provided for this hackathon helped us in overcoming this difficulty.

Accomplishments that we're proud of

We could build a modern and useful web app that incorporates agentic AI.

What we learned

We learned WebMCP (and MCP).

What's next for Explain CLT

We are planning to build similar other apps to demonstrate theorems in mathematics and statistics and theories in physics.

Built With

• css
• html
• javascript
• mcp
• php
• webmcp