The central limit theorem states that as sample size increases, the sampling distribution of the sample mean rapidly approaches the bell shape of a normal distribution, regardless of the shape of the parent population.

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The central limit theorem indeed states that, given a sufficiently large sample size, the sampling distribution of the sample mean will approximate a normal distribution, regardless of the shape of the population distribution from which the samples are drawn.

The Central Limit Theorem States That as Sample Size Increases