Student Response – 20210627F

I am looking for a lively and highly involved discussion of this unit’s topics. Outstanding evidence of learning will be demonstrated by answering the question and fully discussing, challenging, and enhancing the discussion by your fellow students. Would it be worse to make a Type I or a Type II error? [Think carefully before you answer.] Explain what it means, and why. Post your initial response by Thursday at midnight. Also, please reply to two other students’ comments by Sunday Midnight CT. That is a total of 3 entries for each unit’s discussion. Student 1 Would it be worse to make a Type I or a Type II error? [Think carefully before you answer.] Explain what it means, and why. Type I and Type II errors both mean we have made a mistake because we are choosing to go with the opposite of what is true. To reject the null hypothesis that is true is a Type I error. If I fail to reject a null hypothesis that is false, that is a Type II error. (Lind et al., p.279. 2019). The level of significance is important because it is the probability of rejecting the null hypothesis when it is true (Lind et al., p.278. 2019). Example: I’m thinking about changing my car insurance to liability from full coverage. I reviewed my Kelly Blue Book (KBB) value to see if it’s worth it for me to have full coverage (My Car’s Value, n.d.). Let’s say I made the decision to change my insurance to liability even though the full-coverage payout would be significant enough to purchase another vehicle if I were to get into an accident that totaled the car. I created a Type I error by not keeping the full-coverage insurance. After getting in an accident, now I do not have funds to replace the car with. Alpha (α) is the probability of making a type I error (Lind et al., p.279. 2019). Type II error would be if my car was only worth $200 and I kept paying full coverage all year, then when I get in an accident, I would have just wasted paying full coverage because I’m only going to get what my car is worth. Beta(β) is the probability of making a type II error (Lind et al., p.279. 2019). Lind, D. A., Marchal, W. G., Wathen, S. A. (2019). Basic Statistics for Business & Economics (9th ed., pp. 278-279). New York, NY. McGraw-Hill Education.!%2F4%2F1:0)Links to an external site. Adding KBB reference My Car’s Value. Kelly Blue Book. (n.d.). to an external site.. Student 2 Q: Would it be worse to make a Type I or a Type II error? [Think carefully before you answer.] Explain what it means, and why. A: Per the text, a Type I error is rejecting the null hypothesis, H0 when it is true. Whereas a Type II error is not rejecting the null hypothesis when it is false (Lind et al., 2019, page 279). According to an online article, it states “As you analyze your own data and test hypotheses, understanding the difference between Type I and Type II errors is extremely important, because there’s a risk of making each type of error in every analysis, and the amount of risk is in your control (Minitab Blog Editor, 2017)”. After reading the text and online resources, I would think that it depends on the situation that you are in to determine if Type I or Type II is the worse error to make. In regards to a Type I error, I would summarize this as a smokescreen where there is clearly something to address but it is not as severe as it may seem. For a Type II error, you almost have to justify your reasoning for addressing an issue that is not really an issue at all. Crystale Reference: Lind, D. A., Marchal, W. G., & Wathen, S. A. (2019). Basic Statistics for Business & Economics (9th ed.) [E-book]. McGraw-Hill Education.!%2F4%2F1:0)Links to an external site. Minitab Blog Editor. (2017, March 8). Which Statistical Error Is Worse: Type 1 or Type 2? Minitab Blog. to an external site.. Requirements: Complete

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