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Statistical Hypothesis Testing

Checked
aj-geddes·vlatest·testing·MIT

Assess data to determine if observed results are statistically significant using various hypothesis tests.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Statistical Hypothesis Testing, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Statistical Hypothesis Testing" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/statistical-hypothesis-testing/SKILL.md
Scanned
3 files, 262 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to perform rigorous statistical hypothesis testing, allowing them to validate assumptions and determine if observed data differences are statistically significant rather than due to random chance. It supports defining null and alternative hypotheses, selecting appropriate test types (e.g., t-tests, chi-square), and calculating p-values to support data-driven decision-making.

When to use it

  • A/B Testing Analysis: Evaluate whether a new website feature leads to a statistically significant increase in user conversion rates compared to the control group.
  • Quality Control Verification: Confirm if a manufacturing process change has genuinely reduced defect rates or if the observed drop is within normal variance.
  • Survey Data Validation: Determine if demographic differences in survey responses are meaningful or simply artifacts of sample size fluctuations.
  • Scientific Experiment Review: Assess whether experimental results support a new theory by rigorously testing against a baseline null hypothesis.

Key capabilities

  • Formulating clear null and alternative hypotheses based on user input.
  • Recommending the correct statistical test (e.g., z-test, t-test, ANOVA) for specific data distributions.
  • Calculating p-values and confidence intervals to quantify evidence strength.
  • Interpreting results to accept or reject hypotheses with appropriate statistical rigor.

Example prompts

  • "I have two sets of user engagement metrics; run a t-test to see if the difference between them is statistically significant at a 95% confidence level."
  • "Analyze this dataset of sales figures to test the hypothesis that Q4 revenue increased due to our marketing campaign, not seasonal trends."
  • "Perform a chi-square test on these survey responses to check for independence between customer age groups and product preference."

Tips & gotchas

Ensure your input data is clean and properly formatted before analysis, as statistical tests are highly sensitive to outliers and distribution assumptions. Always specify your desired confidence level (commonly 95%) when defining the null hypothesis to avoid misinterpreting p-values.