Experimental Power (And Why It Matters)

Introduction

In this part of the session we are going to look at the issues around covers experimental power (and why it is important). One of the insights revealed by the “replication crisis” is that very often research is underpowered for the effect size of interest (i.e., even if the effect is there, your experiment is unlikely to find it). Even when underpowered studies do reveal the effect of interest, the effect size itself will be over-estimated (thus causing problems for future work that might base their power estimates on this incorrect effect size estimate).

One solution to the challenge is to conduct data simulation as part of the experimental design process. There are many ways to do this using R, and there are several packages on CRAN (the Comprehensive R Archive Network) that provide functions to simulate data for different kinds of designs.

  

  

If you’re interested in reading more about power, you might like to take a look at this classic “Power Primer” paper by Jacob Cohen.

Slides

You can download the slides in .odp format by clicking here and in .pdf format by clicking on the image below.   

  

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