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?

reading your post as a stat major gave me a bit of a twitch when I had to read about them again in your response! im only half joking. your post also talks about how you seek to make statistical terms more accessible, where the language used in a lot of statistics-based predictions can often times be exclusionary. this is true! one cool (but also very bad) thing that may be relevant in your future discussions is highlighting the fact that even amounts researchers and statisticians, i feel like these terms are used, but also represent inaccessibility in a way. it’s sort of hard for me to explain, but nowadays published research only shows p values below 0.05alpha (stats standard), which is a typical sign that results are statistically significant. this is interesting as the p value is commonly adjusted in order to make research look significant, which represents a larger issue on how many researchers see certain p values and confidence intervals as confirmation of results rather than having the added nuance that all research needs. when looking at making stats language more accessible, I think making sure this nuance is baked into such definitions is super important!

I really agree with your comments where you discuss how it is important to both use colloquial terms but also to provide important science and math context. I wrote something similar in my original post because I think the general public should still be informed on important concepts even if they do not fully understand all the specifics. I also think it is interesting how we can make predictions and be quite accurate for large groups of people, however on the individual level we cannot say what will happen. In a group of 1000 people, we can say there was a 10% mortality rate if 100 people died. However, on the individual level it is either 0% or 100%.