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The question asks for the correct interpretation of a 95% confidence interval for the proportion of students who take notes, given by \( 0.14 < p < 0.25 \). 

The correct answer is:

**"There is a 95% chance that the proportion of the population is between 0.14 and 0.25."**

Explanation: 
A 95% confidence interval means that if we were to take many random samples and compute a confidence interval for each sample, 95% of those intervals would contain the true population proportion. This does not mean there's a 95% chance that the true proportion is between the interval for this one specific sample, but instead, it reflects the confidence in the method. However, in everyday language, the last option is the closest to a proper interpretation.

Would you like further details on confidence intervals or any specific points clarified?

Here are some questions that expand on the concept of confidence intervals:
1. How is the margin of error related to the confidence interval?
2. What happens to the width of a confidence interval if the sample size increases?
3. How would changing the confidence level to 99% affect the interval?
4. How do we calculate a confidence interval for proportions?
5. Why can't we say there's a 95% chance that the true proportion falls within this particular interval?

**Tip:** When interpreting confidence intervals, always remember it applies to the population parameter and not individual samples.

A student was asked to find a 95% confidence interval for the proportion of students who take notes using data from a random sample of size n = 89. Which of the following is a correct interpretation of the interval 0.14 < p < 0.25?

Understand how to interpret a 95% confidence interval for the proportion of students who take notes, based on a random sample of size n = 89. This solution covers key statistical concepts such as confidence intervals and the central limit theorem.

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