The Central Limit Theorem states that over a large number of samples, the sampling average of the sample means would be closer to the population mean.
The Central Limit Theorem states that for a random variable X, with mean given by [tex]\mu[/tex] and standard deviation given by [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
This means that over a large number of trials, i.e., of samples from the population, the mean of the sample means will be close to the population mean, with a small standard error, as the standard error is inversely proportional to the square root of the sample size.
More can be learned about the Central Limit Theorem at https://brainly.com/question/25800303
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