Consider a within-subjects experimental design with two conditions. The experimenter ran both a correlated t test (which is the correct analysis) and (incorrectly) an independent-groups t test. The experimenter found the correlated t test was significant whereas the independent-groups t test was not. The experimenter concluded that this was because the mean difference score across subjects was greater than the difference between the means of the conditions. Is this the correct conclusion?

The two mean differences are the same. The correlated t test controlled for differences between subjects and thus reduced the standard error of the difference between the two means.
false
yes
true
no
Correlated t tests generally have more power than independent-groups t tests because the former usually have smaller standard errors of the difference between means, making the t value bigger.

By using each subject as its own control, the standard error is lower.
true
True
false
False
Increasing the correlation between measures:

All else being equal, the higher the correlation, the lower the standard error and therefore the higher the absolute value of t, which decreases p.
true
decreases p
false
increases p
Run the simulation using the default correlation of .5. Sample 20 times and count how many times the difference was significant at the 0.05 level. Repeat this using correlations of .3 and .8.
false
has no effect on p
Run the simulation using the default correlation of .5. Sample 20 times and count how many times the difference was significant at the 0.05 level. Repeat this using correlations of .3 and to .8.
The null hypothesis in a correlated t test is that the population mean difference score is 0.

If the mean difference score is not 0, then the means of the two conditions will not be equal.
true
True
false
False
How does the size of the correlation affect how much change scores vary?

The higher the correlation, the less change scores vary.
true
The higher the correlation, the less change scores vary.
false
The higher the correlation, the more change scores vary.