According to the Central Limit Theorem, the standard error of Distribution of Sa

Question

According to the Central Limit Theorem, the standard error of Distribution of Sample Mwansb( i.e., the average difference between a sample mean and the population mean) will always be (Larger than, Equal to, or Smaller than) the standard deviation of the distribution of scores in the population from which the samples were drawna whenever n> 1.
When n>1, the Distribution of Sample Means will have (less, more, or the same amount of) variability when compared to the distribution of scores from which the samples were drawn?
Please explain the answer According to the Central Limit Theorem, the standard error of Distribution of Sample Mwansb( i.e., the average difference between a sample mean and the population mean) will always be (Larger than, Equal to, or Smaller than) the standard deviation of the distribution of scores in the population from which the samples were drawna whenever n> 1.
When n>1, the Distribution of Sample Means will have (less, more, or the same amount of) variability when compared to the distribution of scores from which the samples were drawn?
Please explain the answer
When n>1, the Distribution of Sample Means will have (less, more, or the same amount of) variability when compared to the distribution of scores from which the samples were drawn?
Please explain the answer

Explanation / Answer

According to the Central Limit Theorem, the standard error of Distribution of Sample Mean ( i.e., the average difference between a sample mean and the population mean) will always be Smaller than the standard deviation of the distribution of scores in the population from which the samples were drawna whenever n> 1.

When n>1, the Distribution of Sample Means will have less variability when compared to the distribution of scores from which the samples were drawn?

Explanation

1) standard error = sd/sqrt(n) , hence when n> 1 sd/sqrt(n) < sd

hence standard error < sd

2)

var(xbar)= sd/sqrt(n) hence when n > 1

var(Xbar) < Var(X)

hence less variability

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