In this post I show how to do simulated-based power analyses that produce a power curve: the obtained power for a range of sample sizes.
A curious thing happened in the field of social psychology: Social psychologists finally realized that statistical power is important. Unfortunately, they then skipped the step of figuring out how to do them correctly. Here I list some papers on power analyses that I hope help in improving the way we do them.
Method sections in academic (social psychology) papers usually consist of the following sections: Participants, Design, Procedure, and Materials. They also tend to be presented in this order. But is this the right order? I don't think so.
Simulation-based power analyses make it easy to understand what power is: Power is simply counting how often you find the results you expect to find. Running simulation-based power analyses might be new for some, so in this blog post I present code to simulate data for a range of different scenarios.