My notes on what I liked about the post, from the announcement:
In this post, Lewis makes a powerful argument that we ought to pay more attention when we find ourselves working with whatever data we can scrounge from data-poor environments, and consider other ways of developing our judgments and predictions.
Some elements of this post I especially appreciated:
The author’s points are applicable to work in many different cause areas, and he explicitly points out ways in which they are more or less applicable depending on the problem at hand.
He opens with a memorable story before making his general points (I expect that this practice will often make Forum posts more memorable, and thus more likely to be applied when they matter).
Rather than simply identifying a problem, he points out ways in which we might be able to overcome it, including a section with “final EA takeaways”; I love to see posts that, when relevant, end with a set of actionable suggestions.
This post was awarded an EA Forum Prize; see the prize announcement for more details.
My notes on what I liked about the post, from the announcement: