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).

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.