Calculating the smallest effect size of interest with GPower

In this post I give a brief instruction on how to calculate the smallest effect size of interest with output from GPower. My instruction is largely based on an excellent blog post from a blog named ‘The 20% Statistician’ by Daniel Lakens. Mr. Lakens is an experimental psychologist at the Human-Technology Interaction group at Eindhoven University of Technology, The Netherlands.

Mean differences or mean changes?

While analyzing data from an experiment, I found myself writing things like ‘The treatment changes the outcome variably by…’ or ‘the treatment leads to changes in the outcome variable’. However, I often thought that talking about changes sounded too ‘dynamic’. After all, I was referring to two different groups of subjects (between-subjects design). What I was doing was to statistically compare means of the outcome variable of different groups. I was ok to talk about changes when referring to within-subject differences, i.e. changes in outcomes for the same subject due to an intervention, but for the between-subjects case, shouldn’t I rather talk about differences instead of changes?