1. For a hypothesis test with an independent-measures t, the larger the two sample variances are, the greater the likelihood that you will reject the null hypothesis. (Points: 1)
True
False
2. Two samples, each with n = 4 scores, have a pooled variance of 32. The estimated standard error for the sample mean difference is v8. (Points: 1)
True
False
3. If other factors are held constant, the larger the larger the two sample sizes are, the greater the likelihood that the independent-measures t test will find a significant difference. (Points: 1)
True
False
4. If two separate samples have M1 = 10 and M2 = 18 with a pooled variance of 16, then Cohen’s d = 0.50. (Points: 1)
True
False
5. A researcher reports an independent-measures t statistic with df = 30. If the two samples are the same size (n1 = n2), then how many individuals are in each sample? (Points: 1)
n = 15
n = 16
n = 30
n = 31
6. If all other factors are held constant, an increase in the size of the sample will increase the likelihood of finding a significant treatment effect. (Points: 1)
True
False
7. If two treatments are both expected to produce a permanent or long-lasting change in the participants, then a repeated-measures design would not be appropriate for comparing the two treatments. (Points: 1)
True
False
8. A researcher would like to compare two treatment conditions with a set of 30 scores in each treatment. If a repeated-measures design is used, the study will require only n = 60 participants. (Points: 1)
True
False
9. A repeated-measures study and an independent-measures study both produced a t statistic with df = 16. How many individuals participated in each study? (Points: 1)
15 for repeated-measures and 17 for independent-measures
15 for repeated-measures and 18 for independent-measures
17 for repeated-measures and 17 for independent-measures
17 for repeated-measures and 18 for independent-measures
10. What is indicated by a large variance for a sample of difference scores? (Points: 1)
a consistent treatment effect and a high likelihood of a significant difference
a consistent treatment effect and a low likelihood of a significant difference
an inconsistent treatment effect and a high likelihood of a significant difference
an inconsistent treatment effect and a low likelihood of a significant difference