Which of the following statements about the sampling distribution of the mean (distribution of sample means) and the central limit theorem (CLT) are true? Select four (4) true statements from the list below: O . The sampling distribution of the mean will be approximately normal when o is large. O . The shape of the sampling distribution is closer to the population shape as the sample size increases. O . If the population is approximately normally distributed, then sample size does not matter for the central limit theorem to apply. O . The smaller the sample size, the larger the standard error. O . The sampling distribution is still assumed to be approximately normal if the underlying population is not normal as long as the sample size is large. O . The sampling distribution is always approximately normal even if the population is not normal. O . From the same population, the mean of the sampling distribution (/x) with n = 10 will be the same as the mean with n = 14. O . The larger the sample size, the larger the difference between the mean of the sampling distribution and the population mean. O . If HI = M and of = then the distribution of sample means is normal. O . From the same population, the standard error of the sampling distribution with n = 15 will be the same as the standard error with n = 27. O . If a population is perfectly normal, then for the distribution of sample means of any size, ME = M and Of = 0