14. 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.