Respuesta :
Answer: C 0.22,
Step-by-step explanation:
We have a normal sampling distribution because we expect at least 10 successes and 10 failures per sample
Using the Central Limit Theorem, it is found that the best approximation of the sampling distribution of p is that it is approximately normal, with mean of 0.22 and standard deviation of 0.0586.
Central Limit Theorem
- The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex]. Â
- For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
In this problem:
- The proportion is of [tex]p = 0.22[/tex].
- Samples of 50 students are taken, hence [tex]n = 50[/tex].
Then, [tex]s = \sqrt{\frac{0.22(0.78)}{50}} = 0.0586[/tex]
The sampling distribution of p is approximately normal, with mean of 0.22 and standard deviation of 0.0586.
To learn more about the Central Limit Theorem, you can take a look at https://brainly.com/question/25868626