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
Onwards
Recap Power analysis through simulation in R Niklas Johannes