Consider a real-world example of something important I was recently wrong about. On February 23, I found out Russia had invaded Ukraine. When disruptive events happen, you must respond immediately. I quickly read what the experts were saying on Foreign Affairs and combined it with my existing knowledge of history, technology and geopolitics. I wrote "There is going to be a war. Ukraine is going to lose. The question is how much, how quickly and on what terms."
Then I remembered that war is unpredictable and war experts are often wrong. I added a qualifiers before publishing. "There is probably going to be a war. Ukraine is probably going to lose. The question is how much, how quickly and on what terms."
A week later, reports of under-equipped soldiers began trickling in and I discovered that Russia's economy was much smaller than I had assumed. I updated my analysis. As the war calcified, I finally had time to research current weapons technology and build my own model of the war from a tactics-level foundation.
When I discovered that experts believed Russia would win, I updated my estimate. I estimated a probability of 0.9 that experts would believe in a Russian victory (conditional upon a Russian victory) P ( B 1 | A ) . I estimated the probability of experts to believe in a Russian victory P ( B 1 ) to be 0.5.