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1. Which of the following is the correct null hypothesis for a repeated-measures t-test?
2. A researcher plans to conduct a research study comparing two treatment conditions with a total of 20 participants. Which of the following designs would produce 20 scores in each treatment?
3. The following data were obtained from a repeated-measures research study. What is the value of MD for these data?
4. A researcher is using a repeated-measures study to evaluate the difference between two treatments. If the difference between the treatments is consistent from one participant to another, then the data should produce.
5. For which of the following situations would a repeated-measures research design be appropriate?
6. A repeated-measures study using a sample of n = 20 participants would produce a t statistic with df =.
7. A researcher obtains t = 2.25 for a repeated-measures study using a sample of n = 10 participants. Based on this t value, what is the correct decision for a two-tailed test?
8. Which of the following possibilities is a serious concern with a repeated-measures study?
9. A repeated-measures study comparing two treatments with n = 4 participants produces MD = 2 and SS = 75 for the difference scores. What is the estimated standard error for the sample mean difference?
10. If all participants in a repeated-measures study show roughly the same 10-point difference between treatments, then the data are likely to produce a significant value for the t statistic.
11. Repeated-measures designs are particularly well-suited to research studies examining
12. For a repeated-measures study, as the sample mean difference increases, the likelihood of rejecting the null hypothesis also increases.
13. A repeated-measures research study comparing two treatments with a set of 10 scores in each treatment will produce a t statistic with df = 18.
14. For a repeated-measures study, if other factors are held constant, then an increase in the sample size will increase the likelihood of finding a significant treatment effect.
15. A repeated-measures test usually is more likely to detect a real treatment effect than an independent-measures test because the repeated-measures design typically has a smaller variance and a smaller estimated standard error.

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