# Module 2 — Bayesian Updating

> **Coming soon.** This module will show — with animation — how adding data narrows a prior into a posterior.

## Preview

Bayes' theorem in one sentence: *the posterior is proportional to the likelihood times the prior.*

Key ideas this module will cover:

- Walking through Bayes' theorem with a coin-flip analogy
- Applying the same logic to TEX86 calibration data
- Why more data = narrower posterior (but not always)
- What happens when data conflict with the prior