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In probability theory, the central limit theorem (CLT) establishes that, in many situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a bell curve) even if the original variables themselves are not normally distributed.
According to the Central Limit Theorem, the mean of the sampling distribution is equal to the population mean as long as the sample size is large enough.
Which of the following statements is true?
According to the Central Limit Theorem, the mean of the sampling distribution is greater than the population mean.
According to the Central Limit Theorem, the mean of the sampling distribution is greater than the standard deviation.
According to the Central Limit Theorem, the mean of the sampling distribution is equal to the population mean as long as the sample size is large enough.
According to the Central Limit Theorem, the mean of the sampling distribution is equal to the standard deviation.
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