Please answer the following questions. For Q2 and Q4 I need to give peer reviewed references in your postings that are also considered as substantive postings which should support your statements. Do list the reference at the end of your postings.

Q1: Think of a claim you have seen on television, on the Internet, or in the newspaper that you believe is misleading. What are the hypotheses associated with it? What other information do you need before you decide if the claim is believable?

Q2: Unemployment rate is defined most basically as the percentage of the total labor force that is unemployed but actively seeking employment and willing to work (Investopedia, Unemployment rate). When hearing this announcement from the Mayor I would first be questioning the overall high rate of unemployment and what is being done at the local level to improve these rates. In my opinion, this is not a significant drop in unemployment regardless the size of the total labor force. For example, if the town has a small population with a work force of only 20,000 a 0.1% drop in unemployment is a difference of 20 jobs. If the total work force is 100,000 a 0.1% drop is 100 jobs. These are insignificant numbers relative to the high number of people out of work.

Q3: In which of the following situations would you want a higher alpha. Explain your response.

1. Testing the defect rate of a new stereo model.
2. Testing a new drug before it is approved for commercial sale in the United States.

Q4: A Type I error occurs when we reject the null hypothesis. A Type II error occurs when we fail to reject (or accept) the null hypothesis when it should be rejected. (Lind, Marchal, & Wathen, 2015).

An example of when a Type II error is more serious than a Type I error is indicated by the hypothesis statements below:

Null Hypothesis: Medication A and Medication B are equally effective.

Alternate Hypothesis: Medication A and Medication B are NOT equally effective.

In this scenario, rejecting the hypothesis that the two medications are equally effective is less serious than a Type II error as patients who were given Medication B under the hypothesis that it was not as effective as Medication A would benefit from the fact that the two drugs were actually equal in effectiveness. A Type II error in this case would fail to reject that both medications were equally effective, and therefore patients may be given one medication that is actually sub par to the other. Their condition or disease would therefore not be treated effectively which could lead to very dire outcomes depending on the condition

An example of a situation when a Type I error is more serious than a Type II error is indicated by the hypothesis statements below:

Null Hypothesis: as a defendant on trial for murder, you are not guilty of the crime.

Alternate Hypothesis: You are guilty of the crime.

In this instance, a Type I error, where we reject the null hypothesis, is far more serious as it would incarcerate an innocent person. Conversely, the Type II error of this hypothesis statement also has implications as it means a guilty person would be found not guilty and would be set free where they could potentially commit more crimes. However our judicial system is actually set up to minimize Type I errors and prefers Type II errors (hence our mantra of innocent until proven guilty and beyond a reasonable doubt).