Ideally, results of a statistical analysis should not depend greatly on a single observation. To check this, it’s a good idea to conduct a sensitivity study. This entails redoing the analysis after deleting an outlier from the data set or changing its value to a more typical value and checking whether results change much. If results change little, this gives us more faith in the conclusions that the statistical analysis reports. For the weight changes in Table 9.4 from the anorexia study (shown again here and also in the Anorexia data file on the text CD), the greatest reported value of 20.9 pounds was a severe outlier. Suppose this observation was actually 2.9 pounds but was incorrectly recorded. Redo the two-sided test of that example, and summarize how the results differ. Does the ultimate conclusion depend on that single observation?
Answer to relevant QuestionsResults of 99% confidence intervals are consistent with results of two-sided tests with which significance level? Explain the connection. Consider the test of H0: The defendant is not guilty against Ha: The defendant is guilty. a. Explain in context the conclusion of the test if H0 is rejected. b. Describe the consequence of a Type I error. c. Explain in ...A study considers if the mean score on a college entrance exam for students in 2010 is any different from the mean score of 500 for students who took the same exam in 1985. Let μ represent the mean score for all students ...For testing H0: p = 1 / 3 (astrologers randomly guessing) against Ha: p > 1 / 3 with n = 116, Example 13 showed that P(Type II error) = 0.02 when p = 0.50. Now suppose that p = 0.35. Recall that P(Type I error) = 0.05. a. ...A Pew Research Center poll in 2010 asked a random sample of 2505 adults about their attitudes and opinions concerning the U.S. government (www.people-press.org). When asked whether they felt content, frustrated or angry, 56% ...
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