1. In confidence intervals, the width of the interval depends only on the variation within the data set. (Points : 1) True False Question 2. 2. A contingency table is a multiple row and multiple column table showing counts in each cell. (Points : 1) True False Question 3. 3. Chi-square tests rarely have type I errors. (Points : 1) True False Question 4. 4. Chi-square tests are more likely to have type II (falsely rejecting the null hypothesis) errors than parametric tests. (Points : 1) True False Question 5. 5. 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 6. 6. The goodness of fit test determines if a data set distribution/shape matches a standard or hypothesized distribution. (Points : 1) True False Question 7. 7. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference. (Points : 1) True False Question 8. 8. The Chi-square test for independence needs a known (rather than calculated) expected distribution. (Points : 1) True False Question 9. 9. For a one sample confidence interval, if the interval contains the μm , the corresponding t-test will have a statistically significant result – rejecting the null hypothesis. (Points : 1) True False Question 10. 10. The null hypothesis for the test of independence states that no correlation exists between the variables. (Points : 1) True False

1. In confidence intervals, the width of the interval depends only on the variation within the data set. (Points : 1)
True
False

Question 2. 2. A contingency table is a multiple row and multiple column table showing counts in each cell. (Points : 1)
True
False

Question 3. 3. Chi-square tests rarely have type I errors. (Points : 1)
True
False

Question 4. 4. Chi-square tests are more likely to have type II (falsely rejecting the null hypothesis) errors than parametric tests. (Points : 1)
True
False

Question 5. 5. 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 6. 6. The goodness of fit test determines if a data set distribution/shape matches a standard or hypothesized distribution. (Points : 1)
True
False

Question 7. 7. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference. (Points : 1)
True
False

Question 8. 8. The Chi-square test for independence needs a known (rather than calculated) expected distribution. (Points : 1)
True
False

Question 9. 9. For a one sample confidence interval, if the interval contains the μm , the corresponding t-test will have a statistically significant result – rejecting the null hypothesis. (Points : 1)
True
False

Question 10. 10. The null hypothesis for the test of independence states that no correlation exists between the variables. (Points : 1)
True
False