Group of answer choices
if the population size is large, then the variance of the sample mean must be small
if the population size is large, then the sampling distribution of the sample mean can be approximated closely by a normal curve
if the sample size is large, then the distribution of the sample sizes can be approximated closely by a normal curve
if the sample size is large, then the sampling distribution of the sample mean can be approximated closely by a normal curve
if the sample size is large, then the standard deviation of a given sample must be small
1 answer
The correct statement regarding the Central Limit Theorem (CLT) is:
if the sample size is large, then the sampling distribution of the sample mean can be approximated closely by a normal curve.
This means that regardless of the shape of the population distribution, the distribution of the sample means will tend to be normally distributed as the sample size increases, typically when the sample size is 30 or more.
