Increasing the correlation between measures:

High correlations can result in much higher power than lower correlations. They do this by lowering the mean square error.
true
increases power.
false
decreases power.
Look at the graph in the simulation and see how power changes as the correlation increases.
false
has no effect on power.
Look at the graph in the simulation and see how power changes as the correlation increases.
Increasing the population variance:

High variance increases the error term and lowers power.
false
increases power.
Change the population variance in the simulation and note the effect on power.
true
decreases power.
false
has no effect on power.
Change the population variance in the simulation and note the effect on power.
Increasing the difference between population means:

The more different the population means, the bigger the effect and the higher the power.
true
increases power.
false
decreases power.
Change the difference in population means in the simulation and note the effect on power.
false
has no effect on power.
Change the difference in population means in the simulation and note the effect on power.
Assuming the hypothesized direction of the effect is correct, a one-tailed test is:

Since the probability is caluclated in only one tail, a one-tailed test results in a lower probability value.
true
more powerful than a two-tailed test.
false
less powerful than a two-tailed test.
Change the test from two-tailed to one-tailed and note the effect on power.
false
has the same power as a two-tailed test.
Change the test from two-tailed to one-tailed and note the effect on power.