Simulation is commonly used by statisticians/data analysts to: (1) estimate variability/improve predictors, (2) to evaluate the space of potential outcomes, and (3) to evaluate the properties of new algorithms or procedures. Over the last couple of days, discussions of simulation have popped up in a couple of different places. First, the reviewers of a paper that my student is working on had asked a question about the behavior of the method in different conditions.
I’ve been going to/giving statistics talks for a few years now. I think everyone in our field has an opinion on the best structure/content/delivery of a talk. I am one of those people that has a pretty specific idea of what makes an amazing talk. Here are a few of the things I think are key, I try to do them and have learned many of these things from other people who I’ve seen speak.
The Twitter universe is abuzz about this article in the New York Times. Arthur Brisbane, who responds to reader’s comments, asks I’m looking for reader input on whether and when New York Times news reporters should challenge “facts” that are asserted by newsmakers they write about. He goes on to give a couple of examples of qualitative facts that reporters have used in stories without questioning the veracity of the claims.