Answer:
Approximately normal
Explanation:
Central Limit Theorem
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 sampling distribution of the sample proportion will be approximately normal with mean
and standard deviation
![s = \sqrt{(p(1-p))/(n)}](https://img.qammunity.org/2022/formulas/mathematics/college/21siyq2l0d9z8pcii2ysmig6q1uk55fvwj.png)
In this question:
As the sample size is above 30, even though the underlying distribution is right-skewed, the shape of the sampling distribution of the sample means will be approximately normal.