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Statistical Analysis Guide

Guides choosing, checking, and reporting statistical tests on research data with effect sizes and APA write-ups

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
7 files

What it does

When installed, the agent walks through a research statistics workflow: framing the question, inspecting the data, choosing an appropriate test (t-tests, ANOVA, chi-square, correlation, regression, non-parametric or Bayesian methods), checking assumptions, computing effect sizes and power, and drafting APA-style results. It also bundles a Python helper script for assumption diagnostics. The skill stresses that statistical screens cannot prove assumptions or scientific validity, so the agent should explain its reasoning and flag limitations.

Great for

  • Researchers or students analyzing experimental or survey data who want a structured, defensible workflow
  • Analysts who need help choosing between tests or planning a sample size
  • Anyone who needs an APA-formatted write-up of results with effect sizes and confidence intervals

Skip it if

  • Replacing a statistician for high-stakes decisions such as clinical or regulatory conclusions, since numerical checks cannot certify assumptions or study validity
  • Users who want a fast p-value without understanding the design, because the skill asks for hypotheses and design details before testing
  • Low-level model fitting, which the skill says is covered by separate statsmodels and pymc skills

Try asking

  • “I have two groups of patient scores with unequal sample sizes. Which test should I use and how do I report it in APA style?”
  • “Check the assumptions for my ANOVA on reaction times and tell me whether the results are trustworthy.”
  • “How many participants do I need per group to detect a medium effect with 80% power?”
  • “Help me write up my regression results with effect sizes and confidence intervals.”
Step-by-step guide: how to use Statistical Analysis GuideInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 7 original files, the MIT licence and an attribution note.

    statistical-analysis.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip statistical-analysis.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip statistical-analysis.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • State your hypothesis, design (paired or independent), and outcome variables before asking for a test, so the agent can select an appropriate method.
  • Create the suggested separate Python environment rather than installing into a shared project, and pin package versions to make results reproducible.
  • Treat the assumption-check output as a prompt for judgment: review plots and study design, and do not switch tests just because a diagnostic p-value crosses a threshold.

What's inside

  • references/assumptions_and_diagnostics.md
  • references/bayesian_statistics.md
  • references/effect_sizes_and_power.md
  • references/reporting_standards.md
  • references/test_selection_guide.md
  • scripts/assumption_checks.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.