Question

Business 210 ch 10 11 12 15 T F 1. A p-value of .008 in hypothesis testing means there is only a .8% chance we could get such sample statistics from the population if the null hypothesis is as stated. Such an event is considered unlikely and we would reject the null hypothesis. T F 2. As a general rule in hypothesis testing, it is always safer to set up your alternate hypothesis with a greater-than or less-than orientation. _____3. If the level of significance is .02 then the chance of making a Type I error is: A. zero B.98% C.2/100 D.19/20 E. cannot be calculated T F 4. When doing two population proportion hypothesis tests, we get our “normalcy” for the z-distribution by keeping the sample sizes from each population greater than or equal to 50. T F 5.If making a Type II error is much more serious than making a Type I error, you would select a .01 rather than a .10 alpha risk. T F 6. When comparing 3 or more population means using ANOVA, we can make the test more robust regarding assumptions by keeping the sample sizes the same in each population. T F 7. It is always more difficult to reject the null hypothesis using the t distribution compared to the z distribution given the same level of significance (alpha risk) and sample size. T F 8. Dependent sample hypothesis tests have a smaller source of variation than independent sample hypotheses tests. T F 9. We could use the z or t distribution to compare more than two populations, but the resulting buildup of Type I error would be an issue. T F 10. The “computer effect” described in one of our daily stats lessons refers to the large amount of useful stats output that can be produced by anyone with statistics software. T F 11. We assume normalcy when using the t-distribution for hypothesis testing. ______12. The “before and after” t-test is appropriate to use when: A. Four samples are compared at once. B. Any two samples are compared C. Independent samples are compared. D. Dependent samples are compared. E. None of the above is correct. T F 13. One advantage of using the p-value method of hypothesis testing is that it allows you to see how close your reject/fail to reject decision was. T F 14. In addition to hypothesis testing, the F-distribution can be used in a test comparing two population variances T F 15. As you reduce your chances of making a Type I error you increase your chances of making a Type II error. T F 16. The p-values used in hypothesis testing for the most part must be calculated by hand using the P-value formula. T F 17. When doing a one population proportion hypothesis tests we must do a normalcy check in Step 3 before we can use the z-distribution. T F 18. A good hypothesis testing strategy is to select a level of significance in Step 2 that minimizes the chances of making the most costly error. T F 19. A “robust” statistics technique is one that gives good decision-making help even if we don’t meet all required assumptions exactly. T F 20. A Type I error occurs when you fail to reject the null hypothesis when it should have been rejected. T F 21. In Step 3 of the hypothesis testing procedure the test statistic is powered by population values. T F 22. As the degrees of freedom change, the shape of the F-distribution curve changes shape, but it always remains positive, starting at 0 and moving off to the right. T F 23. In hypothesis testing, when we fail to reject the null hypothesis we are saying the difference between the sample statistic and the population parameter is not statistically significant. T F 24. An important equation when using the F-distribution to do hypothesis tests comparing 3 populations is: SStotal = SST + SSE. T F 25. When writing up a hypothesis testing conclusion in Step 6 it is OK to say either “we fail to reject the null” or “we accept the null hypothesis”.

Answer 1

Hi, We are supposed to answer four subparts at a time. Since you have not mentioned which subpart to answer, I am answering the first four subparts. Please repost the question with remaining subparts you would like to be answered.

1. A p-value of .008 in hypothesis testing means there is only a .8% chance we could get such sample statistics from the population if the null hypothesis is as stated. Such an event is considered unlikely and we would reject the null hypothesis.

Answer: The statement is true as p-value indicates the rejection region for null hypothesis

2. As a general rule in hypothesis testing, it is always safer to set up your alternate hypothesis with a greater-than or less-than orientation

Answer: The statement is false. Infact it is always safer to set up alternate hypthesis with "not equal to" orientation.\

3.  If the level of significance is .02 then the chance of making a Type I error is: A. zero B.98% C.2/100 D.19/20 E. cannot be calculated

Answer: Level of significance is referred to as the probability of making a Type I error. The correct option would be C.

4. When doing two population proportion hypothesis tests, we get our “normalcy” for the z-distribution by keeping the sample sizes from each population greater than or equal to 50.

Answer: The statement is false because as per rule to the normalcy rule for the z-distribution, the samples sizes from each population should be greater than or equal to 30.