The central limit theorem says that as sample size grows, the sampling distribution of the sample mean approaches a normal distribution under mild conditions, even if the underlying data are not normal. That is why many CFA inference tools use z or t critical values when working with means. It is about the distribution of the sample mean, not a claim that every raw data series becomes normal. A common confusion is applying normal-based shortcuts to tiny samples without care.