Recap

Power analysis through simulation in R

Niklas Johannes

Why this workshop

Designing an informative study is a key skill
A study is rarely informative if it can’t detect what you’re after
Neglecting power means not knowing what our results mean

So why are we here?

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 to turn these processes into data, and (4) use these skills to simulate power for an informative study.

What’s power

Understanding of the logic behind NHST
Intuition about what power is
See why power, perhaps, potentially isn’t just a hoop to jump through

Simulations in R

Understand why simulations are useful
Logic of Monte Carlo Simulations
Basic tools

Effect sizes

Understand the importance of effect sizes
How to formulate a smallest effect size of interest
Know when you don’t have enough information

Alpha, beta, sensitivity

Question the default of \(\alpha\) = 0.05 and power = 80%
Understand how terribly complex designing an informative study is
Know where to turn when you don’t have enough information