Statistics

Power analysis connects sample size, effect size, and the chance of detecting a real improvement.

How distributions become assumptions about data, labels, errors, and model behavior.

A practical introduction to random variables, expectation, and variance for AI experiments.

Regularization controls model complexity by making some parameter values less plausible.

A practical map for choosing statistical tests in NLP, MT, RAG, and LLM evaluation.

Statistics is not just a set of formulas. It is a way to reason about uncertainty, evidence, and trust in AI experiments.