July At The Multiplex Analysis

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Register to read the introduction… He chose to see “The Governator,” which was due to start at 1pm. With 30 minutes to get to the theater in order to make it on time to the movie, Tommy rushed over to the movie theater to try to arrive a few minutes early so he could find a seat, since he usually had problems finding a seat in the dark. Once Tommy arrived at the theatre, he went to the box office to purchase his ticket. He asked again for what time the movie started, and was informed that it began at 1pm by the ticket agent. Since he arrived a bit early, Tommy opted to buy a large bucket of buttered popcorn, a large soda, and a large candy bar, which totaled to another nine dollars. Tommy made it into the theater at 12:58pm, right before the lights went out. At 1pm, the lights dimmed down and the screen started up, but instead of the movie starting, commercials started playing, and continued for the next 20 minutes. Tommy was furious because he had spent $18 dollars on his ticket and refreshments and after 20 minutes, he still didn’t get to see the movie that was intended to start. He left the movie and found a manager, demanding for a refund of his money but the manager refused, starting that it was not the theaters policy to refund any tickets. Tommy expressed dissatisfaction about the lengthy showing of the commercials and informed the theater owner that, since he felt his legal rights had been violated due to the …show more content…
This means, more than likely, the accurate percentage of unhappy customers in your theater consortium’s area is at least 10%.
The null statement is different to our sample result. We have to point out, the first survey is not successful enough to prove the percentage is either smaller than 10%, or at least 10%
Type II Error in the First Survey
Even though the hypothesis test result shows the null statement is true, it still has the possibility that the null statement is not true. Technically, we call this kind of possibility Type II Error.
Here are two kinds of errors in hypothesis tests. They are Type I Error (α) and Type II Error (β). The Type I error will be discussed in the second survey’s conclusion.
Type II Error (β) is the possibility of wrongful accepting of the false null statement in a hypothesis test. Because the sample size is not actually the population, it’s a good possibility to get a polarized sample. So, Type II Error can always exist in the samplings whose sample size is much smaller than the

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