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?