# Experimental and Analytical Pitfalls

## What Problem This Solves

This reference covers the ways charts become statistically misleading even when the rendering is polished.

## When to Use It

Use this when the visualization reflects experiments, model outputs, rollups, or analytical claims with real decision impact.

## Key Takeaways

- Aggregation, smoothing, truncation, and selective baselines can distort conclusions.
- Sample size and missingness often matter as much as central tendency.
- Visual clarity does not guarantee statistical honesty.

## Common Mistakes

- Smoothing away volatility without disclosure.
- Comparing groups with different sample sizes as though they were directly equivalent.

## Adjacent Skills

- `../SKILL.md`
- `../../visualization-strategy-and-critique/SKILL.md`

## Source Links

- [How Charts Lie](https://www.albertocairo.com/books)