Interpreting effect sizes: An Olympic-sized metaphor

First, a pun:

American athlete Athing Mu broke the American record for the 800m. I guess you could say...that Mu is anything but average!! HAHAAAHAHAHHA.

https://twitter.com/Notawful/status/1409456926497423363

Anyway. It is late June 2021, and my Twitter feed is filled with amazing athletes qualifying for the Olympics. Athletes like Sydney McLaughlin.

That picture was taken after McLaughlin a) qualified for the 2021 Olympics AND b) broke the 400m hurdle world record. Which is amazing. 

Now, here is where I think we could explain effect size interpretation. How big was McLaughlin's lead over the previous record?

From SpectrumNews1

McLaughlin broke the world record by less than a second. But she broke the world record so less than a second is a huge deal. Similarly, we may have Cohen's small-medium-large recommendations when interpreting effect sizes, but we always need to interpret an effect size within context. Does a small effect size finding explain more variance than any previously proposed variable? Is the small effect size related to a very inexpensive and easy-to-administer health intervention? Is that medium effect size a sound replication of previous findings but not particularly novel? It all depends on the context, the research question, and the stakes of what you are studying. Similarly, Laughlin's less than second matters a lot in an elite track and field event, but probably not so much at a local 5K race. 

When I teach students to interpret statistical tests, I explain NHST and effect sizes. I think it is important to emphasize the nuance of effect sizes to my junior statisticians.