The problem asks you to interpret the 95% confidence interval for the sample proportion. Here's the step-by-step breakdown of how to interpret it:

Given:

• Sample proportion = 53.2%
• Margin of error = ±7%

The confidence interval can be found by adding and subtracting the margin of error from the sample proportion.

To find the 95% confidence interval:

• Lower bound = 53.2% - 7% = 46.2%
• Upper bound = 53.2% + 7% = 60.2%

Conclusion:

The 95% confidence interval is between 46.2% and 60.2%.

This means the researchers estimate with 95% confidence that the true proportion of college students who use dating apps lies between 46.2% and 60.2%.

Would you like further details on this? Let me know if you have any questions.

Here are 5 related questions to deepen your understanding:

1. What does a 95% confidence level mean in this context?
2. How does increasing the sample size affect the margin of error in a confidence interval?
3. If the confidence level were raised to 99%, how would the interval change?
4. What assumptions must be true for this confidence interval to be valid?
5. How is the margin of error calculated in such a scenario?

Tip: The confidence interval gives a range where the true population proportion likely lies, but increasing confidence (e.g., to 99%) usually increases the width of the interval.