What is gap analysis?

Gap analysis is a concept in personal epistemology: Areas where connections should exist but do not indicate knowledge gaps.

Example: You map your understanding of machine learning and notice that 'gradient descent' connects to 'loss functions' and 'neural networks,' but 'regularization' sits as a near-orphan with a single weak link. You've read about regularization. You can define it. But your graph reveals the truth: you don't understand how it relates to overfitting, bias-variance tradeoff, or model selection. The gap between nodes isn't a formatting problem. It's a learning problem — and now you know exactly where to study next.

This concept is part of Phase 18 (Knowledge Graphs) in the How to Think curriculum, which builds the epistemic infrastructure for knowledge graphs.