# Data Exploration Distribution Analysis

> Sub-skill of data-exploration: Distribution Analysis (+3).

- Skill: `vamseeachanta/data-exploration-distribution-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/data-exploration-distribution-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/data-exploration-distribution-analysis/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/data-exploration-distribution-analysis

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# Distribution Analysis (+3)

## Distribution Analysis


For numeric columns, characterize the distribution:
- **Normal**: Mean and median are close, bell-shaped
- **Skewed right**: Long tail of high values (common for revenue, session duration)
- **Skewed left**: Long tail of low values (less common)
- **Bimodal**: Two peaks (suggests two distinct populations)
- **Power law**: Few very large values, many small ones (common for user activity)
- **Uniform**: Roughly equal frequency across range (often synthetic or random)


## Temporal Patterns


For time series data, look for:
- **Trend**: Sustained upward or downward movement
- **Seasonality**: Repeating patterns (weekly, monthly, quarterly, annual)
- **Day-of-week effects**: Weekday vs. weekend differences
- **Holiday effects**: Drops or spikes around known holidays
- **Change points**: Sudden shifts in level or trend
- **Anomalies**: Individual data points that break the pattern


## Segmentation Discovery


Identify natural segments by:
- Finding categorical columns with 3-20 distinct values
- Comparing metric distributions across segment values
- Looking for segments with significantly different behavior
- Testing whether segments are homogeneous or contain sub-segments


## Correlation Exploration


Between numeric columns:
- Compute correlation matrix for all metric pairs
- Flag strong correlations (|r| > 0.7) for investigation
- Note: Correlation does not imply causation -- flag this explicitly
- Check for non-linear relationships (e.g., quadratic, logarithmic)