statistical-analysis-correlation-is-not-causation
Sub-skill of statistical-analysis: Correlation Is Not Causation (+5).
Best use case
statistical-analysis-correlation-is-not-causation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of statistical-analysis: Correlation Is Not Causation (+5).
Teams using statistical-analysis-correlation-is-not-causation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/correlation-is-not-causation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How statistical-analysis-correlation-is-not-causation Compares
| Feature / Agent | statistical-analysis-correlation-is-not-causation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of statistical-analysis: Correlation Is Not Causation (+5).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Correlation Is Not Causation (+5) ## Correlation Is Not Causation When you find a correlation, explicitly consider: - **Reverse causation**: Maybe B causes A, not A causes B - **Confounding variables**: Maybe C causes both A and B - **Coincidence**: With enough variables, spurious correlations are inevitable **What you can say**: "Users who use feature X have 30% higher retention" **What you cannot say without more evidence**: "Feature X causes 30% higher retention" ## Multiple Comparisons Problem When you test many hypotheses, some will be "significant" by chance: - Testing 20 metrics at p=0.05 means ~1 will be falsely significant - If you looked at many segments before finding one that's different, note that - Adjust for multiple comparisons with Bonferroni correction (divide alpha by number of tests) or report how many tests were run ## Simpson's Paradox A trend in aggregated data can reverse when data is segmented: - Always check whether the conclusion holds across key segments - Example: Overall conversion goes up, but conversion goes down in every segment -- because the mix shifted toward a higher-converting segment ## Survivorship Bias You can only analyze entities that "survived" to be in your dataset: - Analyzing active users ignores those who churned - Analyzing successful companies ignores those that failed - Always ask: "Who is missing from this dataset, and would their inclusion change the conclusion?" ## Ecological Fallacy Aggregate trends may not apply to individuals: - "Countries with higher X have higher Y" does NOT mean "individuals with higher X have higher Y" - Be careful about applying group-level findings to individual cases ## Anchoring on Specific Numbers Be wary of false precision: - "Churn will be 4.73% next quarter" implies more certainty than is warranted - Prefer ranges: "We expect churn between 4-6% based on historical patterns" - Round appropriately: "About 5%" is often more honest than "4.73%"
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