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The question provided is asking about hypothesis testing errors, particularly the type of error described when a sample incorrectly suggests that the food is safe, while it is actually not safe.

Given the hypotheses:
- \( H_0 \): The food is safe.
- \( H_a \): The food is not safe.

The situation described in the question is: "The sample suggests that the food is safe, but it actually is not safe."

This is a **Type II error**. In a Type II error, the null hypothesis (\( H_0 \)) is not rejected when it should be (i.e., the test fails to detect that the alternative hypothesis is true). Here, the test concludes that the food is safe (\( H_0 \)), but in reality, the food is not safe (\( H_a \) is true).

Would you like a detailed explanation of hypothesis testing errors or have any other questions?

Here are five related questions:
1. What is a Type I error in hypothesis testing?
2. How can we reduce the likelihood of making a Type II error?
3. What is the relationship between the significance level (\( \alpha \)) and Type I errors?
4. How do power and sample size affect Type II errors?
5. What are the practical implications of Type I and Type II errors in quality control?

**Tip:** Increasing the sample size of a test can reduce the probability of a Type II error by providing more data to support correct decision-making.

The sample suggests that the food is safe, but it actually is not safe. What type of error is this in a hypothesis test?

In hypothesis testing, learn how to identify Type I and Type II errors using examples. Understand the error made when a sample suggests that the food is safe but is actually not safe, corresponding to a Type II error in this case.

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