The most interesting piece of information I learned actually related to a small bit of a previous lecture. The hockey puck probability test that we did in class, in which the hockey pucks were tested up to 100s of times and had different spreads dependending on the roughness of the board relates to the ball analogy. He says that although he won’t know what will happen in one particular ball or an exact instance, he does know out of 1000 similar balls. This provides an interesting scenario. You can’t guess what happens to 1 individual person in the case of COVID, but you could predict what would happen to a less precise group of 1000.
This interview discusses statistical predictions at points, things like P values and the statistical side of data collection. I would definitely ask a question regarding that aspect. I would probably ask how things like Confidence Intervals play a part in prediction, and although the interviewee states that “ I try to avoid all mathematical language and expressions as much as I can,” I believe that it is still important to inspect within the scientific community. That also poses another interesting question though, how do you make these terms accessible and provide context to predictions that want to know it?