When you reply to two your classmates' posts, respectfully agree/disagree to the post and explain why, while offering an opinion with an example to support it with citations including references. Be sure to do one or more of the following:

• Agree and explain why you agree.
• Respectfully disagree and explain why you disagree.
• Offer an opinion with an example to support it.
• Propose an idea or a suggestion.
• Tell a related personal story.
• Respond to the classmates' or the instructor's questions.
• Pose a question and be sure to include your own answer as an example.
• Explain how the discussion question connects to real-life experiences.

Classmate 1:

The manager of the manufacturing firm is likely concerned with a Type II error. "A type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is false in the population" (Banerjee et al., 2009). A Type II error occurs when a hypothesis or assumption is incorrect and should be rejected but not caught in the experiment. In a scenario, a Type II error would be the manager ignoring that the new software saves the organization thousands of dollars and believing it is not worth purchasing the software. In reality, the new software is effective. It saves the organization time and money, which can result in the organization needing an excellent opportunity to expand their production and innovate to the latest technology to produce at the maximum capacity that can benefit the organization.

For example, in an example of type II error, let us say a pharmaceutical company is conducting a clinical trial to test a new drug to lower high blood pressure; let us say the null hypothesis is that the new drug does not reduce high blood pressure, while the other hypothesis states the drug does reduce high blood pressure. After conducting the trial, the research fails to reject the null hypothesis due to insufficient evidence. However, in reality, the new drug significantly lowers high blood pressure. Due to their conclusion, the pharmaceutical company decided to develop the high blood pressure drug no longer, and many people with high blood pressure will miss this opportunity to control their blood pressure due to the research team's failure to detect the drug's effectiveness. It is also a missed opportunity for the pharmaceutical organization to develop a critical drug that can save many lives.

"A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is true in the population" (Banerjee et al., 2009). It is when a null hypothesis is incorrectly rejected; in this scenario, the manager believes that the new software will save them assembly costs when, in reality, it does not. This can lead to wasting all that money on useless software that does not save them assembly costs. The software company should be more concerned about the Type I error because they are making false promises to this manufacturing company about saving money on the assembly cost when it does not. Therefore, the manufacturer trusts them when purchasing this product. This can ruin the software company's reputation because they are falsely promising. This can lead to bad reviews, manufacturing companies suing them, losing customers and sales, and can cause their organization to fail. Therefore, the software company is more concerned about the TYPE I error so that the reputations are not ruined.

Type I and Type II hypotheses help us understand the errors that may arise when testing different hypotheses to make an important decision, such as purchasing expensive software or creating a new product or drug. As Gimino (2023) mentioned in the article, "It helps to be aware of the potential errors built into hypothesis testing and how they can affect your results." Therefore, it is crucial to be aware of potential issues that may arise along the way rather than making critical decisions solely on hypothesis testing.

Reference:

Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009, July). Hypothesis testing, type I and type II errors. Industrial psychiatry journal. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996198/#:~:text=A%20type%20I%20error%20(false,actually%20false%20in%20the%20population.

Gimino, A. (2023, July 14). Type I and type II errors. Medium. https://medium.com/@andersongimino/type-i-and-type-ii-errors-b9a20f3a7530

Classmate 2:

Given the manufacturing firm is considering the purchase of new software to reduce assembly costs, the manager's main concern will hinge on avoiding a Type I error, and the software company should be more focused on preventing a Type II error. A Type I error arises when the null hypothesis is incorrectly rejected, leading to the false belief that the new software reduces costs when it does not (Hayes, 2022). This mistake could result in wasted resources and negative financial consequences for the manufacturing firm.

A Type II error occurs when the software company mistakenly accepts the null hypothesis, which leads to them missing out on the opportunity to sell their product to the manufacturing firm (Hayes, 2022). In turn, this could lead to lost revenue for the software company.

Establishing the correct level of significance and power before conducting a hypothesis test can assist in reducing errors. The significance level () indicates the likelihood of a Type I error, typically set at 0.05, representing a 5% chance of making a mistake. Power (1-) reflects the test's ability to detect if a true effect exists, with a standard value of 0.9 indicating a 90% chance of detecting statistical significance (Banerjee, 2009). It is also important to note that hypothesis testing does not prove or disprove anything. The process allows us to disprove the null hypothesis and, in turn, support the alternative hypothesis. In instances where the null hypothesis is not rejected, it is accepted by default (Banerjee, 2009).

The manager's focus on avoiding a Type I error is required to ensure that the new software genuinely reduces assembly costs before investing in it. On the other hand, the software company's emphasis on preventing a Type II error emphasizes the importance of not missing out on a potential business deal by failing to recognize the software's potential for saving customer's money based on the system's effectiveness or lack thereof. By carefully determining the appropriate levels of significance and power for the hypothesis test, both the manufacturing firm and the software company can reduce their risks of making costly mistakes.

References:

Banerjee, A., Chitnis, U.B., Jadhav, S.L., Bhawalkar, J.S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Ind Psychiatry J.18(2):127-131. DOI: 10.4103/0972-6748.62274.

Hayes, A. (2022, September 28). Type II error explained, plus example & vs. type I error. Investopedia. https://www.investopedia.com/terms/t/type-ii-error.aspLinks to an external site.