Good Reads, etc.
Why study Statistics?
AI (ChatGPT, etc.) and Statistics
- AMSTAT News 2023-09-01: Judea Pearl, AI, and Causality: What Role Do Statisticians Play?
- Pearl is like a father of causal modeling (establishing causation from observational data). In this interview, he says, "I used to feel safe about AI. What’s the big deal? [...] Once in a while we make a mistake and [...] the world suffers. But most of the time, education works. But with AI [...] teenagers are now a hundred million times faster than you, and they have access to a hundred million times larger space of knowledge. Never in history has there been such an acceleration of the speed of evolution. For that reason, we should worry about it, and I don’t know how to even begin to speak about how to control it."
Some popular-science blogs/podcasts by statisticians:
- Statisticians React to the News (published by the International Statistical Institute)
- Stats + Stories
- Practical Significance (published by the American Statistical Association)
- [more to come]
Some (fun) statistical literature for a popular-science audience:
- Nate Silver's The signal and the noise: why most predictions fail -- but some don't. 2012.
- Alex Reinhart's Statistics done wrong: the woefully complete guide. 2015.
- Edward Tufte's data viz "tetralogy/box set":
- Nathan Yau's data viz books:
- Jeff Rosenthal's popular science exposé on probability theory and random phenomena:
- Aubrey Clayton's Bernoulli's Fallacy: Statistical illogic and the crisis of modern science. 2021.
- Sharon Bertsch McGrayne's The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. 2011.
- thanks to Rob Isdell for suggesting this
- An article from The Guardian on Julian Baggini's How to Think Like a Philospher (2023), and statistical principles show up there!
- [more to come]