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The central limit theorem explains what happens when we repeatedly take averages from a population. Even if the original data is not normally distributed, the distribution of sample means begins to look more like a normal distribution as the sample size increases. This happens because random fluctuations in individual observations start to balance out when averaged together. As a result, the mean of these sample means approaches the true population mean, and the spread becomes more predictable b
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The central limit theorem explains what happens when we repeatedly take averages from a population. Even if the original data is not normally distributed, the distribution of sample means begins to look more like a normal distribution as the sample size increases. This happens because random fluctuations in individual observations start to balance out when averaged together. As a result, the mean of these sample means approaches the true population mean, and the spread becomes more predictable b
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