Two opposite ways a statistical test — or a model's decision rule — can be wrong, and why you can't just dial one of them down for free.
Every statistical test, and every model that has to decide "is this real or not," can fail in exactly two directions. It can cry wolf — declaring an effect that isn't actually there. Or it can miss a wolf that's genuinely present — declaring nothing's there when something is. These aren't the same mistake wearing different clothes. They're opposite failure modes, they're driven by different mechanics, and — critically — reducing one, on its own, tends to make the other one worse.