LLN: average converges to the mean · CLT: that average's distribution becomes Gaussian · same dice, two different claims · runs locally
law of large numbers — running average
central limit theorem — distribution of sample means
LLN: x̄ → μ as n → ∞ · CLT: (x̄ − μ)/(σ/√n) → N(0,1) · different claims — one is about convergence, one is about shape