Effect sizes (ES) are usually required by journal editors to help readers to better understand research outcomes. However, ES should be understood and, when reported, researcher must provide not only which ES metric is used (Hedge’s g, Cohen’s d, and the Glass Δ, η2 and ηp2 …) but also additional information such as sample sizes and statistical power to detect small effect sizes of interest (sesoi) to make them fully informative.:
In this post «A Critical Perspective on Effect Sizes (Quentin A., 2022)» the André Quentin introduces the topic as follows:
In this blog post, I am sharing a slightly-modified version of a presentation I gave to HEC Montreal’s “Research Day on Open Science and Replications in Marketing”. It is called “A Critical Perspective on Effect Sizes”.
I start with a quick refresher on what effect sizes are, discuss the conditions under which effect sizes contain useful information, and conclude by offering some heuristics to evaluate effect sizes.
Enjoy the reading 🙂