Why Do We Need Power? Gaining Insights from Simulation Studies

Schedule

Time	Topic	Exercises
10:00 – 11:10	Simulation-based power analysis
11:20 – 12:30	Power (curves) for t-tests and ANOVA
13:30 – 14:45	Power for baseline/follow-up measurements
15:15 – 16:30	Power for longitudinal data analysis

Abstract

Help, my effect size is too large! Rarely would anyone express a complaint like that. And yet, unrealistically large effect estimates are a widespread artifact resulting from a combination of low power and selection by significance. Conversely, high power is a necessary condition for valid inference. In this workshop, we will illustrate with real-world examples the failures when drawing conclusions from underpowered studies. We will introduce how to calculate the power of a statistical test by computer simulation. In order to gain hands-on experience with implementing these simulations in software, participants should bring their own laptops with R installed.

Content

Power and the significance filter
Simulation-based power analysis with R
Drawing power curves
Power for t-tests, ANOVA, ANCOVA, and mixed-effects models

Prerequisites

Introductory knowledge of statistics
Basic knowledge of R

Software

Participants will need to have installed:

A current R version (https://CRAN.R-project.org/)
An IDE for R (like RStudio or VSCode) or a text editor with syntax highlighting (like Vim or Notepad++)
Additional R package:
- lme4: https://CRAN.R-project.org/package=lme4

Literature

Wickelmaier, F. 2022. “Simulating the Power of Statistical Tests: A Collection of R Examples.” ArXiv. https://doi.org/10.48550/arXiv.2110.09836.