The correct answer is A. You are more likely to be correct using an interval estimate because it is unlikely that a point estimate will exactly equal the population mean.

Explanation:

• A point estimate provides a single value (such as the sample mean) as the best guess for the population mean. However, it is unlikely that this single value will exactly match the true population mean due to natural sampling variability.
• An interval estimate, such as a confidence interval, provides a range of values within which the population mean is likely to fall. Since this range accounts for the possible variability in sampling, it is more likely to capture the true population mean, giving a higher probability of being correct.

This is why interval estimates are generally preferred when estimating population parameters—they provide a better chance of accuracy by accounting for uncertainty.

Do you have any further questions or need details on this?

Here are 5 related questions:

1. What is a confidence interval, and how is it constructed?
2. How do we interpret a 95% confidence interval in the context of population means?
3. What factors affect the width of a confidence interval?
4. Why is the sample size important in constructing an interval estimate?
5. How do point estimates and interval estimates relate to hypothesis testing?

Tip: A narrower confidence interval increases precision but may reduce the probability of containing the true population mean. Consider both precision and confidence when estimating!