Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
10 Cards in this Set
- Front
- Back
|
1. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences). |
1. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences). |
|
|
2. The percent confidence interval is the range having the percent probability of containing the actual population parameter. |
2. The percent confidence interval is the range having the percent probability of containing the actual population parameter. |
|
|
3. The distribution for the goodness of fit test equals k-1, where k equals the number of categories. |
3. The distribution for the goodness of fit test equals k-1, where k equals the number of categories. |
|
|
4. The Chi-square test can be performed on categorical (nominal) level data. |
4. The Chi-square test can be performed on categorical (nominal) level data. |
|
|
5. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant. |
5. A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant. |
|
|
6. In confidence intervals, the width of the interval depends only on the variation within the data set. |
6. In confidence intervals, the width of the interval depends only on the variation within the data set. |
|
|
7. For a two sample confidence interval, the interval shows the difference between the means. |
7. For a two sample confidence interval, the interval shows the difference between the means. |
|
|
8. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference. |
8. If the confidence interval for mean differences contains a 0, the associated t-test would have shown a significant difference. |
|
|
9. Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data. |
9. Statistical significance in the Chi-square test means the population distribution (expected) is not the source of the sample (observed) data. |
|
|
10. The Chi-square test is very sensitive to small differences in frequency differences. |
10. The Chi-square test is very sensitive to small differences in frequency differences. |
