This question involves identifying the type of error in a hypothesis test. Here's the breakdown:

• $H_0$ (Null hypothesis): The food is safe.
• $H_a$ (Alternative hypothesis): The food is not safe.

The scenario given states that "The sample suggests that the food is not safe, but it actually is safe."

This means the null hypothesis (that the food is safe) is true, but the test incorrectly rejected it, suggesting the food is unsafe. This is an example of a Type I error.

Explanation of Errors:

• Type I error: Rejecting the null hypothesis when it is true (in this case, saying the food is unsafe when it is actually safe).
• Type II error: Failing to reject the null hypothesis when the alternative hypothesis is true (saying the food is safe when it is actually unsafe).

So, the correct answer is Type I error.

Would you like more details on hypothesis testing?

Here are 5 related questions:

1. What is a Type II error and how is it different from a Type I error?
2. How can you reduce the likelihood of making a Type I error in a hypothesis test?
3. Why is it important to understand the consequences of both Type I and Type II errors in real-world scenarios?
4. What is the significance level ($\alpha$) in a hypothesis test, and how does it relate to Type I errors?
5. Can you provide an example of a real-world situation where a Type II error would be more problematic than a Type I error?

Tip: Always be mindful of the consequences of both Type I and Type II errors, as they can have different real-world implications depending on the context of the test.