Why do we blindly follow experts when their advice is so often so wrong*? How can we differentiate between good advice and bad? These are just two of the questions David Freedman attempts to answer in Wrong: Why Experts Keep Failing Us (a book that sounds like it could be a nice complement to Kathryn Schulz’s book, mentioned previously).
In an interview with Time, Freedman discusses topics related to his thesis, such as our reaction when confronted with experts, experts and the confirmation bias, the “Wizard of Oz” effect, how animal experiments help to advance science but don’t always provide suitable advice for humans, and what he knows about bad advice and how to recognise it:
Bad advice tends to be simplistic. It tends to be definite, universal and certain. But, of course, that’s the advice we love to hear. The best advice tends to be less certain — those researchers who say, ‘I think maybe this is true in certain situations for some people.’ We should avoid the kind of advice that tends to resonate the most — it’s exciting, it’s a breakthrough, it’s going to solve your problems — and instead look at the advice that embraces complexity and uncertainty. […]
It goes against our intuition, but we have to learn to force ourselves to accept, understand and even embrace that we live in a complex, very messy, very uncertain world.
via @vaughanbell
*Some depressing facts from Freedman’s book, as chosen by Time:
About two-thirds of the findings published in the top medical journals are refuted within a few years. […] As much as 90% of physicians’ medical knowledge has been found to be substantially or completely wrong. In fact, there is a 1 in 12 chance that a doctor’s diagnosis will be so wrong that it causes the patient significant harm. And it’s not just medicine. Economists have found that all studies published in economics journals are likely to be wrong. Professionally prepared tax returns are more likely to contain significant errors than self-prepared returns. Half of all newspaper articles contain at least one factual error.