Tag: Statistics for AI Research

Hypothesis testing is a way to discipline claims, not a ritual for producing p-values.

Linear regression is more than a line: it is a model of signal, noise, assumptions, and explanation.

MAP estimation shows how prior beliefs and observed evidence combine.

Maximum likelihood connects probability models to parameter learning.

When we try many models, prompts, seeds, and metrics, false discoveries become easier than they look.

PCA explains how variance, projection, and representation are connected.