Food inspectors inspect samples of food products to see if they are safe. This can be thought of as a hypothesis test with the following hypotheses.

H0: The food is safe

H1: The food is not safe

The following is an example of what type of error?

*The sample suggests that the food is safe, but it actually is not safe.*

type I

type II

not an error

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.

Food inspectors inspect samples of food products to see if they are safe. This can be thought of as a hypothesis test with the following hypotheses.
H0: The food is safe
H1: The food is not safe
The following is an example of what type of error?
*The sample suggests that the food is safe, but it actually is not safe.*

In this problem, we explore an example of a Type II error in hypothesis testing related to food safety. Learn about how Type II errors occur when the null hypothesis (H₀: 'The food is safe') is not rejected even though the alternative hypothesis (H₁: 'The food is not safe') is true. This explanation is ideal for college-level statistics students.

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