I needto 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 at the minimum you should list our textbook reference.

I will supply the original question then two of my peer's responses. These will be in bold.

Original Question Number 1: Your mayor just announced that the local unemployment rate dropped last month from the prior month. It went from 10.5% to 10.4%. Is this a significant drop? Explain.

1st Peer Response: I would say that it is not a significant drop. The way that the unemployment rate is calculated and who is included in that rate could have an effect.People are classified as unemployed if they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work. You could have people that dropped off last month due to not actively looking for employment, becoming employed or being a student. If it was a small town of 100,000 people it would be a difference of 100. Even if the population was 1,000,000, a 0.1% drop would be 1,000. This does not necessarily mean 1,000 people were employed.

2nd Peer Response: According to Lind, D.A., Marchal, W.G. & Wathens, S.A. (2015) the significance level is also sometimes called the level of risk (p.319). Therefore, we needed to know the level of risk that was applied on this test. If the null hypothesis was that the unemployment rate changed 0.01 or 1% or less and the alternative hypothesis was that the unemployment rate changed morethan 0.01 or 1%. The null hypothesis should not be rejected because the result showed 0.01 or 1% change in unemployment rate. If a change in the unemployment rate were been said 0.02 or 2% as a level of risk the null hypothesis should be rejected. In short, all depends on what level of risk is considered when doing a test.

Original Question number 2:Give an example of a situation in which you believe a Type I Error is more serious than a Type II Error. Give an example of a situation in which you believe a Type II Error is more serious than a Type I Error. In each case, why do you think so?

1st Peer Response: According to Lind, Marchal, & Wathen (2015), By rejecting a true null hypothesis, we committed a Type I error. and a Type II error is committed by Not rejecting the null hypothesis when it is false. (p. 319). A situation in which a Type II Error is more serious than a type I Error is when consumer safety is at stake. An example of this is with the recent recall of Takata air bags installed in vehicles. The air bags can cause shrapnel to fly at individuals in the vehicle and have caused at least nine deaths. By failing to reject the null hypothesis when the airbags did not pass quality assurance standards, a Type II error was committed and individual consumers were harmed. A situation in which a Type I Error is more serious than a Type II Error is when company inadvertently rejects a lumber order that passes quality assurance standards. The error affects the company financial because they will not be able to get the full market value for the lumber. In each case both Type I and Type II errors can have major negative consequences for a company and must be taken seriously for a company to survive and prosper.

2nd Peer Response: One error can be more serious than other as they are inversely related, so it would depend on the situation or the type of test. Their severity is based on the results obtained rather than saying than type I Error is more serious than Type II Error or vice versa. Minitab provides a great example of the severity of Type I and II Errors.

"A type I error occurs if the researcher rejects the null hypothesis and concludes that the two medications are different when, in fact, they are not. If the medications have the same effectiveness, the researcher may not consider this error too severe because the patients still benefit from the same level of effectiveness regardless of which medicine they take. However, if a type II error occurs, the researcher fails to reject the null hypothesis when it should be rejected. That is, the researcher concludes that the medications are the same when, in fact, they are different. This error is potentially life-threatening if the less-effective medication is sold to the public instead of the more effective one

I also need the following answered:

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?