As the size of the sample increases, what happens to the shape of the sampling means?Select one:A. cannot be predicted in advanceB. approaches a normal distributionC. positively skewedD. negatively skewed

As the size of the sample increases, what happens to the shape of the sampling means?

According to the Central Limit Theorem, the sampling distribution of the sample mean becomes approximately normally distributed asthe standard error increasesthe population variance decreasesthe size of the population increasesthe number of  samples drawn increasesthe size of the sample increases

when the sample size increases.， the probability distribution becomes is symmetric or skewed or uniform or peaked

When the population is not normally distributed, the sampling distribution of the mean approximates which of the following? A slight positive skew A normal distribution given a large enough sample A distribution that is not normal A normal distribution

Which of the following accurately describes the effect of increasing the sample size?​Group of answer choices​Increases the standard error and has no effect on the risk of a Type I error​Increases the risk of a Type I error and has no effect on the standard error​Decreases the risk of a Type I error and has no effect on the standard error​Decreases the standard error and has no effect on the risk of a Type I erro