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The problem states that Facebook reports the mean number of friends for adult Facebook users is 338, and a hypothesis test is conducted:

- Null hypothesis \( H_0: \mu = 338 \)
- Alternative hypothesis \( H_1: \mu \neq 338 \)

Since the null hypothesis is **not rejected**, the conclusion is that there is not enough evidence to suggest the mean number of friends differs from 338. Therefore, the correct choices to fill in the blanks would be:

"There is **not** enough evidence to conclude that the mean number of friends is **different than** 338."

This conclusion means that based on the test data, we do not have sufficient evidence to claim that the average number of friends is anything other than 338.

Would you like further details or have any questions?

Here are 5 related questions for deeper understanding:
1. What are Type I and Type II errors in hypothesis testing?
2. How do we calculate the p-value in hypothesis testing?
3. What is the significance level in a hypothesis test, and how does it affect the conclusion?
4. What does it mean if we reject the null hypothesis?
5. Can we use confidence intervals to draw conclusions similar to hypothesis tests?

**Tip:** Always ensure to check the significance level (typically 0.05 or 5%) when interpreting whether to reject or fail to reject a null hypothesis.

Facebook reports that the mean number of friends of adult Facebook users is 338. A test is made of H0: μ = 338 versus H1: μ ≠ 338. The null hypothesis is not rejected. State an appropriate conclusion.

Learn how to conduct hypothesis testing on the mean number of friends of adult Facebook users, with a null hypothesis of μ = 338 and an alternative hypothesis of μ ≠ 338. This problem covers statistical inference, p-values, and the conclusion when the null hypothesis is not rejected.

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