MCP For Claude Marketplace
Chi-Square Test Engine

Chi-Square Test Engine MCP Connector for Claude

A+

Run exact Chi-Square independence tests on contingency tables local. Get CPU-guaranteed chi² statistics and p-values for categorical analysis.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

The Chi-Square test determines whether two categorical variables are independent. Asking an LLM to compute expected frequencies across a matrix and then sum the chi² residuals is a recipe for hallucinated results.

This MCP computes the full test deterministically using jstat. The AI sends the observed frequency matrix, and the engine calculates exact expected frequencies, the chi² statistic, degrees of freedom, and the p-value — all locally on your CPU.

The Superpowers

  • Zero Hallucination: Exact chi² statistics computed deterministically.
  • Automatic Expected Frequencies: The engine builds the entire expected matrix internally.
  • Any Matrix Size: Supports 2x2, 3x3, or larger contingency tables.
  • Data Privacy: Your survey and business data stays local.
statisticsdata-analysiscategorical-datahypothesis-testingmath-enginedata-science

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

calculate_chi_square

Perform exact deterministic Chi-Square tests of independence on categorical contingency tables without LLM math hallucinations

See how to talk to your AI agent using Chi-Square Test Engine.

Is there a statistically significant relationship between user gender and subscription tier?

The Chi-Square test returns chi² = 8.42, df = 2, p-value = 0.015. Since p < 0.05, there is a statistically significant relationship between gender and subscription tier.

Check if the distribution of customer complaints varies by product category.

The p-value is 0.32. We cannot reject the null hypothesis — the complaint distribution appears independent of product category.

Run a chi-square test on this survey data to see if education level affects voting preference.

Chi² = 15.8, df = 6, p-value = 0.015. The result is statistically significant — education level and voting preference are not independent.

It's a matrix showing the frequency distribution of two categorical variables (e.g., rows = Gender, columns = Subscription Tier). The AI will automatically convert your raw data into this format.

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