Answer:
The standard deviation of this sampling distribution would equal 0.0816
Explanation:
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n, the mean of the sampling proportions is p and the standard deviation is

In this problem, we have that:

So

The standard deviation of this sampling distribution would equal 0.0816