To answer this question, we first need to understand what Type I and II errors are. A Type I error is a false positive. In statistical terms, this kind of error occurs when a null hypothesis is true, but gets rejected. A Type II error, on the other hand, is a false negative. This kind of error occurs when a null hypothesis is false, but is not rejected.
Now that we have a decent understanding of what Type I and II errors are, let's take a look at the question. We need to apply a Type II error, a false negative, to the situation that we've been given. The manager of the restaurant wishes to test whether his hypothesis - that customers are served within 4 minutes from the time the order is placed - is valid (or in statistical terms, fails to be rejected).
In a Type II error, the null hypothesis is false but not rejected. In the context of this scenario, the manager may think that the staff is serving customers in an average of under 4 minutes, when in reality this is not true.
If you need any futher explanations of any of the concepts I've tried to explain here, just let me know! Good luck!
- breezyツ
