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The question in the image asks about how sample means relate to the population mean and whether sample means are good estimates of population means.

Here’s how the blanks should be filled:

1. **The sample means** *target* the population mean. This means that the sample mean tends to approach the population mean as the sample size increases.
   
2. **In general, sample means** *do* make good estimates of population means because the mean is an **unbiased** estimator.

So the filled-in statement would be:
- "The sample means **target** the population mean. In general, sample means **do** make good estimates of population means because the mean is an **unbiased** estimator."

Would you like further details or explanations on this? 

Here are 5 relative questions:
1. Why is the sample mean considered an unbiased estimator?
2. How does the sample size affect the accuracy of the sample mean?
3. What is the difference between biased and unbiased estimators?
4. In what scenarios might a sample mean not be a good estimate of the population mean?
5. How does the central limit theorem relate to sample means?

**Tip:** Increasing the sample size tends to make the sample mean a more accurate estimator of the population mean because variability decreases as more data is included.

Do the sample means target the population mean? In general, do sample means make good estimates of population means? Why or why not?

Explore why sample means are considered good estimators of the population mean, focusing on concepts like unbiased estimators and the Law of Large Numbers. This explanation is suitable for college-level statistics students.

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