Paul Meehl dedicated his life to figuring out who was better at making decisions – human experts or data. After decades of research he concluded with finality that data-driven algorithms are consistently better at making decisions than humans. Meehl summarized his life work with this:
There is no controversy in social science which shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one. When you are pushing over 100 investigations, predicting everything from the outcome of football games to the diagnosis of liver disease, and when you can hardly come up with a half dozen studies showing even a weak tendency in favor of the clinician, it is time to draw a practical conclusion.
The “practical conclusion” was that data outperforms the human brain, every time. Which begs the question, if using data to make decisions leads to such superior results, why don’t we?
The short answer is this: the human brain does a poor job of thinking with data. While most of us pride ourselves on sound judgements and unbiased thoughts, there’s abundant evidence showing just how irrational we really are. Here’s why.
1. Physical need trumps critical thinking
The human brain conducts two very different types of thinking. System 1 is the realm of unconscious reasoning. It’s fast, instinctive, and easy. It’s the residue of our evolutionary past—good at tasks like making fight or flight decisions, scanning the horizon for motion, and making simple assumptions. System 2, by comparison, is the new kid on the block. It handles conscious reasoning. It’s controlled, slow, and logical. It also takes an enormous amount of effort.
System 1 is a beast born of survival, it’s tougher, older, and much stronger. Things like hunger, cold, and danger trump our System 2 desires to think like reasonable humans. In Thinking, Fast and Slow, Daniel Kahneman covers the findings of Proceedings of the National Academy of Sciences, a study that analyzed the decision of parole judges. In the study, most parole applications are denied: only 35% of the requests were approved. Unfortunately for the applicants, it turns out that the single biggest factor determining approval was the time of day the request was processed.
65% of parole requests were granted right after the judges ate, with rates dropping steadily in the two hours before the next snack break. If a judge, whose job revolves around hearing and analyzing facts and evidence, can be influenced by biology, the rest of us don’t stand a chance! We’re all tripped up by the demands of System 1—in fact, your brain is probably lying to you right now.
2. Fast perceptions trump quantitative analysis
In Here’s Looking at Euclid the author, Alex Bellos, catches up with a linguist, Pierre Pica, recently returned from a trip studying the Munduruku, a tribe of people who live a mostly numberless existence. The Munduruku people count to five, but no higher. It’s not that they can’t, it’s that they have no need to do so.
The Munduruku offer a hint at why it’s still so difficult for us to comprehend numbers. Our brains were designed for survival, not quantitative precision:
Faced with a group of spear-wielding adversaries, we needed to know instantly whether there were more of them than us. When we saw two trees we needed to know instantly which had more fruit hanging from it. In neither case was it necessary to enumerate every enemy or every fruit individually. The crucial thing was to be able to make quick estimates of the relative amounts.
This kind of “fast perception” is wonderfully suited for the challenges of early mankind, but it falls a bit short when trying to evaluate things like top-performing marketing channels, the most effective exercise regimen, or the charities that have the greatest impact on improving the human condition. Our brains, like the Munduruku, are designed for 1,2, many – not mathematical nuance. Be aware that your brain is naturally inclined to make quick assumptions rather than conduct careful analysis.
3. Thinking with numbers is mentally taxing
Because our brains are not naturally included to work like calculators, using numbers requires a significant amount of mental energy. In the famous Invisible Gorilla experiment or Selective Attention Test, study participants are asked to watch a video of a basketball game while doing difficult arithmetic manipulations. Consistently, half of the participants won’t notice the person in the video wearing a gorilla suit. “Numbers mode” takes an enormous amount of concentration, so much that while in that mode we’re susceptible to missing otherwise obvious changes in our environment.
4. Thinking with numbers is physically exhausting
In the early years of his career Daniel Kahneman conducted research on biological responses to quantitative thought. Experiments consisted of asking subjects to tackle increasingly difficult math problems. At the toughest part of the experiment, subjects’ pupil dilation would be increase by 50% and heart rates would increase by 7 beats per minute. The second a subject would give up on a problem, resigning themselves to their inability, pupils would retract to normal size and heart rates would level out.
These simple biological responses indicate the strain System 2 thinking has on our bodies. It’s not only mentally tiring, it’s physically exhausting. Our brains were designed to only use System 2 when absolutely necessary to conserve energy! Laziness is an evolutionary advantage when energy is scarce.
None of this is to say that using data is impossible, only that in using data, humans have to overcome our hardwired biological tendencies. People have been using it for centuries to accomplish amazing things. Galileo used data to figure out how the universe works, Florence Nightingale used data to dramatically decrease deaths in military hospitals, George Washington Carver used data to improve the lives of impoverished farmers.
It can be done, but don’t expect it to just happen. If you want to be data driven, you have to recognize that your brain will be trying to work against you. Be intentional. No one ever said being data-driven was easy, but it’s the only way to get results.