Question

You should start this part of the In-Depth Analysis writing down the statistical hypothesis. The statistical hypothesis gives the logical framework for making a statistical decision regarding the researchers' hypothesis. Write the hypothesis using the formal statistical hypothesis notation as follows - H0: Statement of null hypothesis H1: Statement of alternative hypothesis Next, write a 250 word  paragraph discussing the potential consequences of a type I error. You should explain at least two possible consequences. Also explain how serious you see these consequences as being. In this paragraph you need to make a recommendation about what you believe the level of significance should be in order to declare the data as being statistically significant. When I ask you to think about consequences of type I error, I am asking you to think very deeply about a complex idea. The payoff for this effort is a deep understanding that will stay with you long term. In the situation of hypothesis testing we are ultimately going to make a decision, and like many decisions we make every day, this decision will come with consequences. Unlike many decisions in life, statistical decisions are either right or wrong. This is related to the logic and mathematics at play, specifically the distribution concept. When we think about level of significance, we should think probability. The Greek letter  and the English phrase "level of significance" are the same thing, a probability. They are the probability that the decision resulted in a type I error. The simplest way to deal with this probability from a teacher's point of view is just to tell students to memorize that  is always 5%. All things considered, this is a pretty simple thing to memorize. But doesn't the question "why?" come to mind? Memorizing "" is not critical thinking. Answering the question "why?" is critical thinking which is much more difficult. How might we answer this question? The answer is to consider the consequences of actually making a type I error. ​

Answer 1

H0: The null hypothesis is that there is no significant difference between the two groups.

H1: The alternative hypothesis is that there is a significant difference between the two groups.

A type I error occurs when the null hypothesis is rejected, but it is actually true. This means that the researcher concludes that there is a significant difference between the two groups when there is not. The consequences of a type I error can be serious.

One possible consequence of a type I error is that it can lead to incorrect decisions being made. For example, if a new drug is being tested and a type I error occurs, the drug may be approved for use even though it is not actually effective. This can have serious consequences for patients who rely on the drug to treat their condition.

Another possible consequence of a type I error is that it can lead to wasted resources. For example, if a company invests a significant amount of money in a new product based on the results of a study that later turns out to be a type I error, the company may have wasted resources that could have been used more effectively elsewhere.

Given the potential consequences of a type I error, it is important to choose an appropriate level of significance. The level of significance represents the probability of making a type I error. A commonly used level of significance is 0.05, which means that there is a 5% chance of making a type I error. However, the appropriate level of significance will depend on the specific situation and the consequences of a type I error. In general, the level of significance should be set low enough to minimize the risk of a type I error, but not so low that it becomes difficult to detect meaningful differences between the groups.