Good Reads, etc.

Why study Statistics?

1. Megan Yu's PLOS ECR blog entry. 2016-05-31.

2. Weissgerber et al.'s PLOS Biology article. 2016.

 

AI (ChatGPT, etc.) and Statistics

1. '[AI] is perceived as “intelligence” and yet relies fundamentally on statistics.' From The Conversation 2026-02-08. 
2. Business Insider 2026-01-10: AI isn't making us smarter — it's training us to think backward, an innovation theorist says (source website)
3. The Guardian 2026-01-02: Google AI Overviews put people at risk of harm with misleading health advice
4. The Guardian 2025-11-18: What AI doesn't know: we could be creating a global 'knowledge collapse'
5. South China Morning Post 2025-12-17: Professor loses key role over retracted paper [that] contained references to non-existent publications generated by AI. 
6. The Guardian 2025-11-10: ‘It shows such a laziness’: why I refuse to date someone who uses ChatGPT
7. 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."
8. Elena Naumova: Artificial intelligence and data analytics competencies for public health professionals. 2024-06-26.
   • Naumova's discussion is cross-cutting and not specific to public health professionals.
9. Cathy O'Neil: Weapons of math destruction : how big data increases inequality and threatens democracy. 2016.
10. [more to come]

 

Some popular-science blogs/podcasts by statisticians:

1. Stats + Stories
2. Practical Significance (published by the American Statistical Association)
3. Statisticians React to the News (published by the International Statistical Institute)
4. [more to come]

 

Some (fun) statistical literature for a popular-science audience:

1. Nate Silver's The signal and the noise: why most predictions fail -- but some don't. 2012.
2. Alex Reinhart's Statistics done wrong: the woefully complete guide. 2015.
3. Edward Tufte's data viz^# "tetralogy/box set":
   • The Visual Display of Quantitative Information. 1983.
   • Envisioning information. 1990.
   • Visual explanations: images and quantities, evidence and narrative. 1997.
   • Beautiful evidence. 2006.
4. Nathan Yau's data viz^# books:
   • Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. 2011
   • Data Points: Visualization That Means Something. 2013
5. Jeff Rosenthal's popular science exposé on probability theory and random phenomena:
   • Struck by Lightning: The Curious World of Probabilities. 2005.
   • Knock on Wood: Luck, Chance, and the Meaning of Everything. 2018.  
   • "I’m a statistics professor who became embroiled in the world of online chess drama." 2025.
6. Aubrey Clayton's Bernoulli's Fallacy: Statistical illogic and the crisis of modern science. 2021.
7. 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
8. An article from The Guardian on Julian Baggini's How to Think Like a Philospher (2023), and statistical principles show up there!
9. [more to come]

 

 

 

 

# Really, please never use dynamite plots, REALLY. And check out Elena Naumova's guide on the principles of data visualization from 2024-04-15.