Whenever you use statistical inference. confidence intervals or hypothesis tests

Question

Whenever you use statistical inference. confidence intervals or hypothesis tests, you are acting as if your data are a random sample from a population. The central limit Theorem guarantees that averages are more variable than individual observations. You can always tell if the probability distribution of a random variable, X. is legitimate if the probabilities assigned to each value of X sum to 1. Suppose a 95% confidence interval for the population proportion of students at UW - Whitewater who regularly drink alcohol to excess is (0.61, 0.67). The inference you can make is that the true proportion of students who drink to excess is 2/3. The confidence interval for a mean, given a random sample of n = 200, is invalid of the population distribution is bimodal (i.e. has 2 humps). If you have a volunteer sample instead of a random sample, then a confidence interval for a parameter is still completely reliable as long as the sample size is large enough, i.e. larger than about 30. If you decide to reject the null hypothesis using alpha = 0.01, then you also would reject the null hypothesis using alpha = 0.05. A study about the change in weight on a new diet reports a P-value = 0.043 for testing the null hypothesis that there is no weight change versus a non-directional alternative that there is weight change. If the authors instead reported a 95% confidence interval for mu. then the interval would have contained 0. A 95% confidence interval for mu = population mean IQ is (96k, 110). So. in the test of H_0: mu = l00 versus H_A: mu notequalto 100, the lest statistic |Z| > 1.96. The P-value is the probability that the null hypothesis is true. The difference between the population distribution of a variable and the sampling distribution of a statistic is that the first summarizes the population while the second summarizes the sample data. Hypotheses arc always stated in terms of sample statistics. If the P-value for a statistical hypothesis test is as small as or smaller than a pre-specified significance level, alpha, then one should retain the null hypothesis.

Explanation / Answer

18. True
19. False
20. True
21. True
22. True
23. False
24. False
25. False
26. False
27. False
28. False
29. False
30. False

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