1. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences). (Points : 1) True FalseQuestion 2. 2. Chi-square tests are parametric in nature – requiring data that fit a specific distribution/shape. (Points : 1) True FalseQuestion 3. 3. The null hypothesis for the test of independence states that no correlation exists between the variables. (Points : 1) True FalseQuestion 4. 4. Point estimates provide less confidence in indicating a parameter’s value than a confidence interval. (Points : 1) True FalseQuestion 5. 5. For a two sample confidence interval, the interval shows the difference between the means. (Points : 1) True FalseQuestion 6. 6. The Chi-square test measures differences in frequency counts rather than differences in size (such as the t-test and ANOVA). (Points : 1) True FalseQuestion 7. 7. For a one sample confidence interval, if the interval contains the ?m , the corresponding t-test will have a statistically significant result – rejecting the null hypothesis. (Points : 1) True FalseQuestion 8. 8. 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 FalseQuestion 9. 9. The probability that the actual population mean will be outside of a 98% confidence interval is (Points : 1) 1%2%4%5%Question 10. 10. The Chi-square test for independence is an extension of the goodness of fit test to see if multiple groups are distributed according to expected distributions for each variable. (Points : 1) True False