# Statistical Foundations B–F — Expanded Jupyter Tutorials

English-first, Chinese terminology where helpful. Based on the supplied `mclp.ipynb` layout, with **hidden quiz answers** using `<details><summary>Answer</summary>`.

Each notebook now opens with **Why Study This?** (general scientific motivation), conceptual questions, detailed mathematics and derivations, a separate visualization-driven general-purpose experiment, and retains the original hands-on labs, sensitivity work, and the STARM crosswalk.

| Chapter | Foundation | Typical applications |
|---|---|---|
| B | Statistical hypothesis testing | Clinical trials, A/B testing, spatial rule inference |
| C | Multiple hypothesis testing | Omics, spatial scans, high-throughput discovery |
| D | Spatial statistics | Disease mapping, ecology, event locations |
| E | Spatio-temporal processes | Climate, epidemics, environmental monitoring |
| F | Statistical simulation and validation | Methods research, power, calibration, robustness |

Run sequentially using Python 3 with `numpy`, `scipy`, `matplotlib`; some original labs may use `pandas`. Answers are collapsed in Jupyter frontends that support HTML `<details>` elements.