[en] Power analysis is a statistical method used to determine the likelihood that a study will detect an effect of a specified size, given a particular sample size and significance level. By understanding the relationship between sample size, effect size, and significance level, researchers can design studies with sufficient statistical power to detect meaningful effects. This practical session serves as a primer on power calculations in R. We will discuss the core principles of power analysis, focusing on identifying what to power for (the main research question and effect size) and learning flexible rules for various types of power calculations. Participants will learn hands-on how to set up and run basic power analyses using the R programming language. Examples will be drawn from health science literature. For hierarchical, nested experimental designs without analytical power solutions, we’ll focus on simulation-based approaches. By the end of this session, participants will have a toolkit of methods for conducting accurate power analyses across different experimental frameworks, enabling them to design robust studies that are well-powered for their scientific goals.
