• STATISTICAL
  • INSIGHT
  • DATA
  • HUMAN

How to validate survey segment relationships with your AI

Connect this MCP to your AI client once. After that, your agent handles the statistical math to find real patterns in your survey data.

Ask AI about this page

Short answer

Can my AI tell if my survey segments are actually different?

Yes. Your AI uses the Chi-Square Test Engine to calculate exact p-values for your survey data. This tells you if a relationship between a demographic and a preference is statistically significant or just noise. You'll see the specific outcomes below.

The results you get

Statistical certainty

The AI turns raw survey counts into actionable math.

STATISTICAL

Exact p-values

The AI calculates precise significance levels. You'll know exactly how much confidence to put in a specific segment's behavior.

INSIGHT

Validated relationships

The AI identifies which demographic traits actually drive specific survey responses. It separates real trends from random coincidences.

DATA

Contingency tables

The AI organizes your raw counts into structured tables. This makes the relationship between variables easy to see and verify.

HUMAN

Strategic decisions

The AI provides the math, but you decide if the findings matter for your business. You interpret the significance to guide your next move.

The workflow

What your AI does when the data arrives.

Your AI handles the heavy lifting of statistical computation.

  1. Data ingestion

    Your AI reads your survey results or demographic counts from your provided files or sheets.

    calculate_chi_square

  2. Frequency mapping

    The AI builds a contingency table to map out how many people fall into each specific category combination.

    calculate_chi_square

  3. Statistical testing

    The AI performs the math to determine if the observed frequencies deviate enough from what you'd expect by chance.

    calculate_chi_square

  4. Result interpretation

    The AI presents the p-values and explains whether the relationship meets your significance threshold.

    calculate_chi_square

Try it

Copy these to start.

Paste these prompts into your AI client to begin.

Starting points

Swap the bracketed text for your actual file names or specific survey questions.

jstat Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_3a81o44g9lK8eLsDLspiymfXnhfQOUzKsOVzwpSJ/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — jstat capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "chi-square-test-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_3a81o44g9lK8eLsDLspiymfXnhfQOUzKsOVzwpSJ/mcp"
    }
  }
}
Copy into chat04

  • Run a chi-square test on the survey_results.csv file to see if age groups are linked to product preference.

  • Check if there is a significant relationship between the 'Region' column and 'Subscription Type' in my Google Sheet.

  • Using the data in customer_feedback.json, tell me if gender is a significant factor in how people rate our service.

  • Calculate the p-value for the relationship between user segment and feature usage based on the attached survey data.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Chi-Square Test Engine once, then ask.

Just click the link to connect the MCP to your client. Your credentials stay encrypted, and you can start running tests immediately.

Connect Chi-Square Test Engine to your AI

FAQ

How the testing works

  • 01

    Can the AI change my original survey data?

    No. The AI only reads your data to perform calculations. It cannot edit, delete, or overwrite your original files.

  • 02

    Does the AI decide if a result is 'important'?

    No. The AI provides the p-value. You are the one who decides if that value is significant enough to act on for your specific needs.

  • 03

    What kind of data can I use for these tests?

    You can use any categorical data, such as demographics, yes/no responses, or multiple-choice selections.

  • 04

    Can the AI run a T-test instead?

    No. This specific MCP is built for chi-square tests on categorical data. It does not perform T-tests or other continuous variable analyses.

  • 05

    Do I need to format my data in a specific way?

    It helps if your data is in a clean table format like a CSV or a spreadsheet, so the AI can easily map the categories.

  • More questions about Chi-Square Test Engine? The Connector page answers them. See everything the Chi-Square Test Engine Connector can do

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