The central limit theorem says the sampling distribution of a mean approaches a normal shape as the sample grows, whatever shape the underlying data has. Average enough independent draws and the average behaves predictably, even when single values do not.
That is why it matters at work. Revenue per user is wildly skewed, with a long tail of big spenders and a pile of zeros. The mean of ten thousand users still behaves close to normal, so standard error formulas, z-tests, and confidence intervals stay usable on ugly data.
It does not fix everything. The heavier the tail, the larger the sample you need before the approximation is any good. When a handful of users can move the mean by themselves, trimming or capping extreme values works better than trusting the theorem and reporting a falsely tight interval.
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