The situation described is an example of a Type II error.

• A Type II error occurs when the null hypothesis (H₀: "The food is safe") is not rejected, even though the alternative hypothesis (H₁: "The food is not safe") is actually true. In this case, the sample suggests the food is safe (failure to reject H₀), but in reality, it is not safe (H₁ is true).

Would you like more details on hypothesis testing or errors? Here are some related questions:

1. What is the difference between Type I and Type II errors?
2. How can the probability of making a Type II error be reduced?
3. What is the role of significance level (α) in a hypothesis test?
4. What is the power of a test in hypothesis testing?
5. How does sample size affect Type I and Type II errors?

Tip: In hypothesis testing, a Type I error is often denoted by the significance level (α), while the probability of a Type II error is denoted by β.