Reviews of the psychological literature suggest that many studies lack sufficient statistical power to detect effects of interest. Increased attention to statistical power by journal editors, reviewers, and funding agencies has led to a need for researchers to consider power carefully when designing studies. Our goal is to present an overview of issues that influence statistical power in the context of traditional research designs and analytic methods. We then extend the discussion of statistical power to complex designs and analyses providing readers with sources useful for evaluating power in the design stage of conducting research. Finally, we advocate the use of simulation and Monte Carlo methods as a flexible general strategy for designing research studies with adequate statistical power.