Will I love Calling Bullshit?

One of the things access to the internet and social media has done is that we increasingly only see what we are exposed to. Therefore, people "doing their own research" often end up wandering into rabbit holes of misinformation. How does one differentiate fact from fiction, when AI is only going to make things worse? In a world where anyone can call themselves an expert, Bergstrom teaches us critical thinking and how we can understand that big data is not always correct, but can lead to false conclusions.

There are lots of book that claim to help you think. There is even a "smart thinking" category in some bookstores, presumably to distinguish the books it contains from the ones that promote dumb thinking. I have read many such books (and written a couple myself). This one is my favorite. It is packed with concrete examples and fun to read. You will genuinely be smarter after reading this book.

This book highlights the many ways in which we are frequently misled by data – falling for claims of causation when the data only supports correlation, not recognising that data may be a selected sample that only presents a small part of the picture, and ignoring researchers’ incentives to hand-pick the methodology that gives them the results they want.

This rigorous but highly readable book explains how to spot the common ways in which people mislead us, either deliberately or unintentionally, and call it out in a constructive and professional way.

The title is provocative but justified because so much of the “evidence” that we are bombarded with daily is bullshit. This is a wonderful compilation of statistical mistakes and misuses that are intended to persuade readers to be skeptical and to show them how to recognize bullshit when they see it.

Most people and organizations aspire to be data-driven, yet some of the most fascinating “discoveries” and stories are incorrect. 

The authors use the term Bullshit; I prefer Twyman’s law: any figure that looks interesting or different is usually wrong—look at very surprising results with skepticism. For instance, the adage that correlation doesn’t imply causation is well known, but correlations don’t help sell new articles, so many correlations are told as causal stories.

The book elucidates pitfalls from uncontrolled experiments, common causes, p-hacking, and selection bias, and amazing “classical” stories are debunked.

Example: If you claim your toothpaste reduces plaque “by… show more.