  This is a writeup of lectures on "statistics" that have evolved from the
initial version for the 2009 Hadron Collider Physics Summer School at CERN to
versions for other venues and, most recently, for the African School of
Fundamental Physics and Applications in 2024. The emphasis is on foundations,
using simple examples to illustrate the points that are still debated in the
professional statistics literature. The three main approaches to interval
estimation (Neyman confidence, Bayesian, likelihood ratio) are discussed and
compared in detail, with and without nuisance parameters. Hypothesis testing is
discussed mainly from the frequentist point of view, with pointers to the
Bayesian literature. Various foundational issues are emphasized, including the
conditionality principle and the likelihood principle.
