1. The goodness of fit test null hypothesis states that the sample data does not match an expected distribution. (Points : 1)
True
False
Question 2.2. The Chi-square test for independence needs a known (rather than calculated) expected distribution. (Points : 1)
True
False
Question 3.3. The Chi-square test measures differences in frequency counts rather than differences in size (such as the t-test and ANOVA). (Points : 1)
True
False
Question 4.4. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant. (Points : 1)
True
False
Question 5.5. The distribution for the goodness of fit test equals k-1, where k equals the number of categories. (Points : 1)
True
False
Question 6.6. The Chi-square test results having expected values of less than 5 in a cell may produce a greater likelihood of having type I errors (wrongly rejecting the null hypothesis). (Points : 1)
True
False
Question 7.7. The Chi-square test is very sensitive to small differences in frequency differences. (Points : 1)
True
False
Question 8.8. The goodness of fit test can be used for a single or multiple set (rows) of data, such as comparing male and female age distributions with an expected distribution at the same time. (Points : 1)
True
False
Question 9.9. The percent confidence interval is the range having the percent probability of containing the actual population parameter. (Points : 1)
True
False
Question 10.10. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences). (Points : 1)