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This problem pertains to hypothesis testing, specifically identifying types of errors. Here's the key information and the solution:

### Hypothesis Definitions:
- **Null hypothesis (\( H_0 \))**: \( \mu = 100 \) (the classes are not successful).
- **Alternative hypothesis (\( H_1 \))**: \( \mu > 100 \) (the classes are successful).

### Context:
You are told that the classes are **actually successful** (\( H_1 \) is true), but the test concludes that the classes might not be successful (\( H_0 \) is not rejected). 

### Error Identification:
This situation describes a **Type II error**:
- A **Type II error** occurs when the null hypothesis (\( H_0 \)) is not rejected, even though it is false (and the alternative hypothesis, \( H_1 \), is true).

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### Final Answer:
The error is a **Type II error**.

Would you like more details or clarification?  

Here are 5 questions to extend the topic:
1. What distinguishes a Type I error from a Type II error?
2. How can the probability of making a Type II error be reduced?
3. What is the role of the significance level (\( \alpha \)) in hypothesis testing?
4. How does sample size influence Type II error probability?
5. Can you explain the relationship between power and Type II error?

**Tip:** Always ensure that the test's power (\( 1 - \beta \), where \( \beta \) is the Type II error rate) is sufficiently high to minimize Type II errors.

You are told that the classes are actually successful, but the test concludes that the classes might not be successful. What type of error is this?

Learn about Type II error in hypothesis testing, where the null hypothesis is not rejected despite being false. This problem involves understanding the concepts of null and alternative hypotheses, as well as the consequences of Type II error. Suitable for students in Grades 11-12.

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