Point estimates provide less confidence in indicating a parameter’s value than a confidence interva10l. (Points : 1) True
False
| Question 2. 2. 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 3. 3. A confidence interval is generally created when statistical tests fail to reject the null lhypothesis – that is, when results are not statistically significant. (Points : 1) |
True
False |
| Question 4. 4. The goodness of fit test null hypothesis states that the sample data does not match an expected distribution. (Points : 1) |
True
False |
| Question 5. 5. Chi-square tests are more likely to have type II (falsely rejecting the null hypothesis) errors than parametric tests. (Points : 1) |
True
False |
| Question 6. 6. Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data. (Points : 1) |
True
False |
| Question 7. 7. 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 8. 8. The Chi-square test is very sensitive to small differences in frequency differences. (Points : 1) |
True
False |
| Question 9. 9. In confidence intervals, the width of the interval depends only on the variation within the data set. (Points : 1) |
True
False |
| Question 10. 10. For a two sample confidence interval, the interval shows the difference between the means. (Points : 1) |
True
False |
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