The Central Limit Theorem (CLT)

Connect the standardized mean to its normal limit while keeping raw-data shape, sampling precision and approximation accuracy distinct. Use original simulations, rare-event scenarios and counterexamples to challenge simplistic sample-size rules.

Prerequisites: Sampling distributions, standard errors and finite-variance probability models. Includes single-select and multiselect practice with worked explanations and misconception analysis. Questions are independently answerable; local learning order progresses from foundations to analytical challenges.