Understanding the Conditions

Suppose we want to construct a confidence interval for [tex]p[/tex] with [tex]n=50[/tex] and [tex]\hat{p}=0.9[/tex]. Is the large counts condition met?

A. Yes, [tex]n \hat{p}[/tex] is at least 10.
B. Yes, [tex]n(1-\hat{p})[/tex] is at least 10.
C. Yes, both [tex]n \hat{p}[/tex] and [tex]n(1-\hat{p})[/tex] are at least 10.
D. No, [tex]n \hat{p}[/tex] and [tex]n(1-\hat{p})[/tex] are not both at least 10.

Answer :

To determine if the large counts condition is met, we need to ensure that the values of [tex]\( n\hat{p} \)[/tex] and [tex]\( n(1-\hat{p}) \)[/tex] are both at least 10. Here's how you can verify these conditions:

1. Identify the values:
- Sample size [tex]\( n = 50 \)[/tex]
- Estimated proportion [tex]\( \hat{p} = 0.9 \)[/tex]

2. Calculate [tex]\( n\hat{p} \)[/tex]:
- This is the product of the sample size and the estimated proportion.
- [tex]\( n\hat{p} = 50 \times 0.9 = 45 \)[/tex]

3. Calculate [tex]\( n(1-\hat{p}) \)[/tex]:
- First, find [tex]\( 1-\hat{p} \)[/tex], which is [tex]\( 1 - 0.9 = 0.1 \)[/tex].
- Then, calculate the product of the sample size and this value.
- [tex]\( n(1-\hat{p}) = 50 \times 0.1 = 5 \)[/tex]

4. Check the large counts condition:
- For the condition to be met, both [tex]\( n\hat{p} \)[/tex] and [tex]\( n(1-\hat{p}) \)[/tex] should be at least 10.
- [tex]\( n\hat{p} = 45 \)[/tex] is at least 10, so this part of the condition is met.
- However, [tex]\( n(1-\hat{p}) = 5 \)[/tex] is not at least 10, so this part of the condition is not met.

Since [tex]\( n(1-\hat{p}) \)[/tex] is not at least 10, the large counts condition is not fully satisfied. Thus, the correct answer is:

No, [tex]\( n\hat{p} \)[/tex] and [tex]\( n(1-\hat{p}) \)[/tex] are not both at least 10.