The Central Limit Theorem (CLT) is a fundamental principle in statistics that states that the distribution of the sample means approaches a normal distribution, regardless of the shape of the population distribution, as the sample size becomes larger. Specifically, if you take a sufficiently large number of random samples from a population and calculate their means, these means will form a distribution that approximates a normal distribution with a mean equal to the mean of the population () and a standard deviation equal to the population standard deviation () divided by the square root of the sample size (), represented as .
This theorem is crucial because it allows statisticians to make inferences about population parameters even when the underlying population distribution is not normal. The CLT justifies the use of the normal distribution in various statistical methods, including hypothesis testing and confidence interval estimation, particularly when dealing with large samples. In practice, a sample size of 30 is often considered sufficient for the CLT to hold true, although smaller samples may also work if the population distribution is not heavily skewed.
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