I just finished The Signal and the Noise, and I recommend it. Below are a few of my Kindle highlights, all direct quotes from the book. They’re not necessarily representative of the whole. I’m sure you can find a more complete review elsewhere.
- There isn’t any more truth in the world than there was before the Internet or the printing press.
- Absolutely nothing useful is realized when one person who holds that there is a 0 percent probability of something argues against another person who holds that the probability is 100 percent.
- The goal of any predictive model is to capture as much signal as possible and as little noise as possible. Striking the right balance is not always so easy, and our ability to do so will be dictated by the strength of the theory and the quality and quantity of the data. In economic forecasting, the data is very poor and the theory is weak, hence Armstrong’s argument that “the more complex you make the model the worse the forecast gets. In climate forecasting, the situation is more equivocal: the theory about the greenhouse effect is strong, which supports more complicated models. However, temperature data is very noisy, which argues against them. Which consideration wins out? We can address this question empirically, by evaluating the success and failure of different predictive approaches in climate science. What matters most, as always, is how well the predictions do in the real world.
- There are some academics who are quite content to be relatively anonymous. But there are other people who aspire to be public intellectuals, to be pretty bold and to attach nonnegligible probabilities to fairly dramatic change. That’s much more likely to bring you attention. [Related post.]
- Schmidt received numerous calls from reporters asking him what October blizzards in New York implied about global warming. He told them he wasn’t sure; the models didn’t go into that kind of detail. But some of his colleagues were less cautious, and the more dramatic their claims, the more likely they were to be quoted in the newspaper.
- When we advance more confident claims and they fail to come to fruition, this constitutes much more powerful evidence against our hypothesis. We can’t really blame anyone for losing faith in our forecasts when this occurs; they are making the correct inference under Bayesian logic.
- The dysfunctional state of the American political system is the best reason to be pessimistic about our country’s future. Our scientific and technological prowess is the best reason to be optimistic. We are an inventive people. The United States produces ridiculous numbers of patents, has many of the world’s best universities and research institutions, and our companies lead the market in fields ranging from pharmaceuticals to information technology. If I had a choice between a tournament of ideas and a political cage match, I know which fight I’d rather be engaging in—especially if I thought I had the right forecast.