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 my opinion, a Type II error would be worse.

Explanation:

Thera are two types of error in statistical hypothesis test

Type I error: happens when a null hypothesis is true, but is rejected.

Type II error: happens when a null hypothesis is false, but fails to be rejected.

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.

i would prefer to go with the news article for nate, its recent and updated and stores a lot of information

answer; competency based pay;