The Central Limit Theorem is a foundational mathematical principle in probability theory stating that the average of $n$ independent random samples drawn from any distribution (with mean $\mu$ and standard deviation $\sigma$) will approximately follow a normal distribution centered at the true mean $\mu$ with a standard deviation of $\sigma/\sqrt{n}$, as the sample size $n$ grows large. In machine learning, this theorem explains why the empirical error of a classifier evaluated on a test set converges to its true population error at an asymptotic rate of $\mathcal{O}(1/\sqrt{n})$.