People don't generally understand how statistical metrics are supposed to work. The whole point of statistics is to give you reasonable expectations for the possibilities of a space that is both full of variation but well-explored based on what particulars you know already. The problem is that most people don't realize the arithmetic mean is intended to give you the expectations associated with not knowing essentially anything. It's the map you give a stranger in a strange land (am I using that reference right? I haven't actually read the book yet); the way I explain how to get somewhere in my hometown to a family member isn't just not the same as the way I would to somewhere near my new job, but runs a worryingly high chance of making things less clear than if they just tried to do it themselves (well, at least, before the days of google maps. Why does technology have to be so good at ruining metaphors?). When you have, or will have, information, working without it is just asking to get tripped up. This the brilliance of the adjustable cockpits example: The Air Force leadership assumed they couldn't set cockpits to fit their pilots because they didn't currently know which pilot would fly which plane, forgetting that no plane takes off in the Air Force without the leadership telling a particular pilot to get in a particular plane. Once they adopted the life-like "What do I know about Y given what I (will) know about X" approach over the card-table-like "What do I know about Y given I can't know anything about X", seemingly untouchable problems immediately resolved out almost on their own.

We see this immediately and intuitively from the great conditional graph in this comment: https://news.ycombinator.com/item?id=31958102