As we have seen, conducting a hypothesis test based on a sample of data is not a fail-safe endeavor. As managers we need to weigh the pros and cons of each type of error. The movie theater manager wants to test whether showing old classics changes the average satisfaction rating of his customers. The null hypothesis is that there is no change. Describe what the type I and type II errors would be in the context of this problem. Which would be worse for the theater manager: making a type I error or a type II error? Why?

In this case, a Type I error would mean that the average satisfaction didn't change, but the study gives you that it actually changed. This conclusion would provoke efforts to improve a situation that in theory worsened, but it is not. It would represent more than anything an unnecessary effort.

A type II error would be, in my opinion, a worst situation. It would mean that, since the analysis tells us that the situation has not changed and the average satisfaction remains the same, no action would be taken, when in reality the satisfaction really changed and we could not detect it. It would represent ignoring a situation that could have changed for the worse.

soneyedesola44 soneyedesola44 · Answer 2 · 2025-03-29T04:29:50-04:00

I think making a type II error will be worse for the theater manager.

Further Explanation

In the scenario presented, type I error would imply that the average satisfaction of his customer's rating didn't change but the analysis revealed that it changed, which is certainly not true. This conclusion would trigger you to embark on an Extra effort to better improved the situation, which is not in any way the true state of the situation. In this case, such an effort will not be necessary

Also, in the case presented, i think the type II error will be worse, because it revealed that the situation has not changed, which also implies that the average satisfaction is still the same and wouldn't be necessary to take any further action, whereas, in reality, the average satisfaction has really changed but the available analysis isn't showing the true state of things.

we have two types of errors in statistical hypothesis and these two are

Type I error
type II error

Type I error refers to fault that takes place during the process of hypothesis testing when a null hypothesis is true but rejected, even when it is accurate and should be considered

Type II error occurs in the hypothesis testing process when a null hypothesis is false but not rejected.

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KEYWORDS:

type II error
type I error
hypothesis testing
theater manager
null hypothesis