Statistical power is the probability of finding an effect if there truly is one to find. Unfortunately, it is often treated as a hoop to jump through so you can appease the ethics board or a reviewer. That's a shame, because knowing what you power for means you know what your study is all about. Power analysis lets you calculate the sample size needed for your next study or the sensitivity of your already conducted study. Attending this workshop will give you a deeper understanding of what you're after in your empirical analyses, rather than following heuristics. What heuristics? Ideally, after this workshop, you can immediately see what's problematic about a sentence like this:

 

“Following previous work (random citation), we calculated power for a two-way ANOVA. Relying on a medium effect size, we needed 26 people per group for 80% power.”

 

Instead of heuristics, we'll learn how to use simulations for calculating statistical power - but far more important, you'll learn about the thinking behind a simulation to make all aspects of your study explicit and transparent. You'll have everything you need to run a power simulation in R after half a day. But hopefully, you'll come away from this workshop with a good understanding of why you simulated your power a certain way.

 

In terms of analysis, we won't go beyond (repeated measures) interactions (aka regressions and ANOVAs). We won't cover more complicated designs like multilevel models-although after visiting this workshop, you'll have all tools to do that if you want to.

Participants will find the course useful if:

The goal of the workshop is for you to (1) have an understanding of the philosophy behind using data to test claims, (2) get an intuition of how data generation processes work, (3) learn the technical skills in R to turn these processes into data, and (4) use these skills to simulate power for an informative study.

Each of the following blocks has a several learning outcomes. They are: