Foundations of Data Science
The central limit theorem states that the sampling distribution of the sample mean will approach a normal distribution as the sample size increases, regardless of the original distribution's shape. This theorem is crucial because it allows for the use of normal probability models to make inferences about population parameters based on sample statistics, simplifying many statistical analyses and making it easier to apply various estimation techniques.
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