Please make sure you\'re answer is 100% correct. I\'ve had people answer similar
ID: 3319103 • Letter: P
Question
Please make sure you're answer is 100% correct. I've had people answer similar questions on here within minututes after posting my question with full confidence and they were incorrect. Thank you in advance.
Q4.20: To verify whether there are negative consequences of taking a new type of medicine, 3000 tests were conducted. Assume that the null hypothesis is that the new medicine has no negative effects and the alternative hypothesis is that the new medicine is potentially harmful, 31 of these tests gave significant results at a 1% significance level. What can we say about the potential harmfulness of the new medicine? A Since we did multiple tests at the 1% significance level, we should expect that about 1% of the tests would give the Type I error, and so in our case we can be sure that the new medicine is harmful. Since 31 tests gave significant results, we can be quite sure that the new medicine is harmful. Since we did multiple tests at the 1% significance level, we should expect that about 1% of the tests B C would give the Type I error, and so in our case we can be sure that the new medicine is harmless. D Since we did multiple tests at the 1% significance level, we should expect that about 1% of the tests would give the Type II error, and so in our case we can be sure that the new medicine is harmful. E Since 31 is very small compared to 3000, we can be sure that the new medicine is harmless.Explanation / Answer
C option is right
Type I and type II errors. ... In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is incorrectly retaining a false null hypothesis (also known as a "false negative" finding).