statistical-analysis-central-tendency
Sub-skill of statistical-analysis: Central Tendency (+3).
Best use case
statistical-analysis-central-tendency is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of statistical-analysis: Central Tendency (+3).
Teams using statistical-analysis-central-tendency 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/central-tendency/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How statistical-analysis-central-tendency Compares
| Feature / Agent | statistical-analysis-central-tendency | 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: Central Tendency (+3).
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
# Central Tendency (+3) ## Central Tendency Choose the right measure of center based on the data: | Situation | Use | Why | |---|---|---| | Symmetric distribution, no outliers | Mean | Most efficient estimator | | Skewed distribution | Median | Robust to outliers | | Categorical or ordinal data | Mode | Only option for non-numeric | | Highly skewed with outliers (e.g., revenue per user) | Median + mean | Report both; the gap shows skew | **Always report mean and median together for business metrics.** If they diverge significantly, the data is skewed and the mean alone is misleading. ## Spread and Variability - **Standard deviation**: How far values typically fall from the mean. Use with normally distributed data. - **Interquartile range (IQR)**: Distance from p25 to p75. Robust to outliers. Use with skewed data. - **Coefficient of variation (CV)**: StdDev / Mean. Use to compare variability across metrics with different scales. - **Range**: Max minus min. Sensitive to outliers but gives a quick sense of data extent. ## Percentiles for Business Context Report key percentiles to tell a richer story than mean alone: ``` p1: Bottom 1% (floor / minimum typical value) p5: Low end of normal range p25: First quartile p50: Median (typical user) p75: Third quartile p90: Top 10% / power users p95: High end of normal range p99: Top 1% / extreme users ``` **Example narrative**: "The median session duration is 4.2 minutes, but the top 10% of users spend over 22 minutes per session, pulling the mean up to 7.8 minutes." ## Describing Distributions Characterize every numeric distribution you analyze: - **Shape**: Normal, right-skewed, left-skewed, bimodal, uniform, heavy-tailed - **Center**: Mean and median (and the gap between them) - **Spread**: Standard deviation or IQR - **Outliers**: How many and how extreme - **Bounds**: Is there a natural floor (zero) or ceiling (100%)?
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