Whenever you use statistical inference, confidence intervals or hypothesis tests
ID: 3232602 • Letter: W
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 0.43 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 (96,110). So, in the test of H_0 : mu = 100 versus H_a mu = 100, the test statistic |Z| > 1.96. The P-value is the probability that the null hypothesis is true.Explanation / Answer
18 . Yes,That's the importatant thing to notice that statistical inference always that sample randomly comes from the population.
19. No, CLT just tells that mean always tend to normal distribution but not about more variability.
20. No, That is not true in many of the cases mostly in continous probability cases.
21. Yes, we can make such inference as true population mean is in between the confidence interval/
22. No, that doesn't change with population distribution only depend on sample size.
23. No, that sample will be biased so it can't be said true in nature.
24. Yes, it is obvious.
25. No, as the confidence interval is 95 % and P- value is 0.043 so less than alpha (0.05) so we can reject the null hypothesis and can say that 95% CI doesn't contain 0.
26. No, Test statistic Z < 1.96 because here we can not reject the null hypothesis so Z < Zcritical
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