The Central Limit Theorem says that the sampling distribution of the sample mean is approximately normal if
- all possible samples are selected.
- the sample size is large.
- the standard error of the sampling distribution is small.
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The Central Limit Theorem says that the mean of the sampling distribution of the sample means is
- equal to the population mean divided by the square root of the sample size.
- close to the population mean if the sample size is large.
- exactly equal to the population mean.
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The Central Limit Theorem says that the standard deviation of the sampling distribution of the sample means is
- equal to the population standard deviation divided by the square root of the sample size.
- close to the population standard deviation if the sample size is large.
- exactly equal to the standard deviation.
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Samples of size 25 are selected from a population with mean 40 and standard deviation 7.5. The mean of the sampling distribution of sample means is
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Samples of size 25 are selected from a population with mean 40 and standard deviation 7.5. The standard error of the sampling distribution of sample means is
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