Distribution Analysis

---
name: data-exploration-distribution-analysis
description: 'Sub-skill of data-exploration: Distribution Analysis (+3).'
version: 1.0.0
category: data-analytics
type: reference
scripts_exempt: true
---

# 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)