Sampling Distribution
In statistics, a sampling distribution is the probability distribution of a statistic based on a random sample. It is used to make inferences about the population from which the sample was drawn. The sampling distribution is important because it allows us to estimate the parameters of the population, such as the mean and standard deviation, based on the sample statistics. As the sample size increases, the sampling distribution becomes more normal and the standard error of the mean decreases. This means that larger samples provide more accurate estimates of the population parameters. The central limit theorem states that the sampling distribution of the mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.
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