# 12.4: The Central Limit Theorem

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The Central Limit Theorem tells us that as sample sizes get larger, the sampling distribution of the mean will become normally distributed, even if the data within each sample are not normally distributed.

We can see this in real data. Let’s work with the variable AlcoholYear in the NHANES distribution, which is highly skewed, as shown in the left panel of Figure ??. This distribution is, for lack of a better word, funky – and definitely not normally distributed. Now let’s look at the sampling distribution of the mean for this variable. Figure 12.2 shows the sampling distribution for this variable, which is obtained by repeatedly drawing samples of size 50 from the NHANES dataset and taking the mean. Despite the clear non-normality of the original data, the sampling distribution is remarkably close to the normal.

The Central Limit Theorem is important for statistics because it allows us to safely assume that the sampling distribution of the mean will be normal in most cases. This means that we can take advantage of statistical techniques that assume a normal distribution, as we will see in the next section.

This page titled 12.4: The Central Limit Theorem is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.