Don’t Trust Your Gut

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What’s in it for me? Make better decisions through data analysis.

Let’s say you have a particularly difficult decision to make. What do you do? Make a list of pros and cons? Canvas friends and family for their opinions? Go online and search for advice in various forums? Turn to self-help books?

What happens if you do all those things and you still can’t come to a firm decision? Well, according to conventional wisdom, there’s one decision-making strategy that you can still use. And the good news is, it’s often framed as one of the simplest yet most effective strategies around: you can trust your gut. When you follow your intuition, the thinking goes, you’ll almost always make the right choice.

But here’s the thing: your gut is probably wrong.

Instead of trusting your gut, you should be following the data. These days, there’s more data available than ever before, and data analysis techniques are more sophisticated than they’ve ever been. And, time after time, the data shows that counter-intuitive decisions, choices that go against the prevailing tides of wisdom, are more effective than the so-called intuitive choices we make when we follow our gut.

You might not be feeling inspired to say goodbye to instinct and intuition – yet. But after this Blink to Don’t Trust Your Gut, you just might be.

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Wall Street, Silicon Valley, and pro sports are all across data-driven decision-making.

When it comes to making important decisions, we’re often told to “go with our gut.” But that might not be the best advice. Contrary to popular opinion, you can’t always trust your gut. In fact, every decision you make based on gut feeling could be costing you – big time.

The best way to make effective decisions isn’t to follow your instincts; instead, you’re better off basing your decision-making process on data.

That’s right, data. Thanks to the internet, we have huge stores of data, from Wikipedia profiles to Facebook relationship status updates, at our fingertips. Advances in data analysis techniques mean it’s easier than ever to generate insights from these enormous data sets. Whatever your dilemma – whether you’re weighing up proposing to your partner or wondering about moving to a new city – odds are that some enthusiastic data scientist has crunched the numbers and generated findings that can help you make the right decision. And, when you look at the data for yourself, you might be surprised to find that your gut feeling was actually way off base.

Don’t believe me? From pro baseball to Wall Street, data analysis has underpinned a lot of winning decisions.

The Oakland A’s had one of the lowest payrolls in the league when they reached the playoffs in 2002 and 2003. Rather than trying to draft star players with high batting averages, manager Billy Beane looked at the data. The data showed that other metrics, such as time spent on-base or slugging average, were both better predictors of match success than batting average and undervalued by the market. With these insights, Beane was able to assemble a first-class team with a comparatively low budget.

In Silicon Valley, data is king. One Google designer quit the company over a dispute about which shade of blue to use in an ad link. The designer wanted to go with their intuition, choosing a shade that fit their design sensibilities. The data showed a different shade would lead to a higher click conversion rate. And guess what? Conversion metrics prove Google was right to trust their data over their designer.

Renaissance Technologies is one of the most prestigious, not to mention profitable, hedge funds on Wall Street. Founder James Simon started the company with something more valuable than seed money: he purchased a huge set of raw financial data. Simon, and a team of expert mathematicians, mined the dataset for patterns and trends. Now, every trade Renaissance makes is data-driven. And, in the years since its founding, Renaissance has delivered a 66 percent return since it was founded – pretty impressive, when you consider the S&P 500 delivered a 10 percent return in that same time period.

Ready to ignore your gut? The next four chapters will show you how to make better decisions with data.

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Don’t hit the gym – to get more Tinder matches, try data!

Let me tell you about a friend of mine, who I’m going to call Eddie. Eddie’s single and, like a lot of single people who’d prefer to be coupled, he’s downloaded a few dating apps and uploaded his profile. Unfortunately, he hasn’t gotten a lot of matches. How can he boost his success rate?

Eddie’s gut feeling tells him that the more conventionally attractive someone appears, the better luck they’ll have on dating platforms. So, he could choose to hit the gym, whiten his teeth, and get a haircut. Would that be the right decision?

Well, yes and no says Christian Rutter, a data whiz who’s analyzed tens of millions of profiles on the dating platform OkCupid. Christian’s research confirms Eddie’s gut feeling – dazzlingly good-looking people do tend to outperform their more average-looking counterparts on these platforms. But, here’s the catch. Eddie is nice-looking. If he worked on his appearance, well, he’d be nic-er looking. He still wouldn’t be Brad-Pitt-level attractive. And according to Rutter’s research, unless you’re extremely attractive, your appearance won’t sway the number of matches you get.

But that doesn’t mean Eddie can’t hack the system to get more matches. See, Rutter also found that it’s not just extremely good-looking people who’ve found success on OkCupid – it’s also extremely tattooed people, and people with extremely unusual haircuts or styles of dress. Basically, if you look extreme in any way, you’re more likely to provoke a strong reaction from prospective matches. If Eddie got a face tattoo he’d provoke a much stronger reaction from the platform’s users. Lots of people would probably be turned off by his profile. But the people who were interested would be really interested. Interested enough to match with him.

Luckily for Eddie, the data shows there are some alternative options to up his online dating success rate.

He can earn more. Easier said than done, perhaps, but for heterosexual daters, men who earn in the $150,000–$200,000 bracket attract 8.9 percent more matches than men in the $35,000–$50,000 bracket. High-earning women, on the other hand, can only expect to attract 3.9 percent more matches than their lower-earning peers.

So, rich men attract more matches. That’s hardly counter-intuitive. But the data also shows us that for men, job title is just as important as salary, if not more so. An accountant might earn $150,000 but a lawyer who also earns $150,000 tends to attract more matches. The same goes for doctors, soldiers, policemen, and firefighters. Teachers and hospitality workers, on the other hand, are out of luck.

The data also shows that similarity, not difference, is the most attractive trait. A study of over one million eHarmony matches finds that profiles with shared descriptors – for example, profiles where both singles described themselves as “adventurous” or both described themselves as “introverted” – were far more likely to match.

Finally, Eddie can ditch the idea that opposites attract and search for someone similar to him.

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The data on happily ever after.

These days, AI technology is pretty impressive. AI can defeat human masters at games like chess and Go. It can predict emerging health issues, like Parkinson’s disease, before the patient notices that same issue for themselves. It can reliably pinpoint when social unrest will break out simply by analyzing discussions on Twitter.

But it can’t explain why some romantic relationships succeed where others fail.

Scientists have tried. Data expert Samantha Joel pulled together a dataset of over 11,000 couples, including data on their physical appearances, ages, salaries, interests, and values. Among all that data, Joel didn’t find any reliable predictors of romantic success.

Does that tell us that there’s no science to building a happy, lasting relationship?

It might. Or, it might tell us that when it comes to looking for a long-term partner, we’re simply looking for the wrong things. All the factors that Joel and her team studied map pretty closely to the factors that we know are predictors of desirability. Just think back to the last chapter: extremely attractive, high-earning people get more matches. We’re more likely to match with people whose interests and values align with our own. And yet all these factors that we prioritize in dating turn out to have little to do with long-term romantic success.

What gives? Joel had the same question. And she did ultimately uncover a few key qualities to look for in a long-term partner. One is satisfaction – you’re more likely to be happy with someone long-term if they’re already happy in most areas of their life. Another is a growth mindset – basically, if your partner believes they can learn new skills, hone their talents, and improve themselves as a person, then they have a growth mindset.

Now, you can’t really tell from a 30-second profile perusal if someone has these important qualities. You need to spend time with them and get to know them to make that call.

Ready for the romance hack? In the dating market, certain people are more desirable than others: for example, a man between 6’3” and 6’4” is 65 percent more likely to match with a woman than a man between 5’7” and 5’8”. A 6’ man earning $62,500 is just as desirable as a 5’6” man earning $237, 500. Those extra six inches of height are worth a whopping $175,000. Here’s the thing. A short man is just as likely as a tall man to have a growth mindset or a good level of satisfaction. If a tall man isn’t more likely to make a woman happy, why would a single woman concentrate her efforts on dating tall men who are, on the dating market, overvalued? That single woman should concentrate on “undervalued assets” – like short men, who are considered less conventionally desirable.

No matter what dating pool you’re in, this is solid advice: if predictors of desirability don’t correlate with predictors of long-term happiness, pinpoint who the undervalued assets are, and target them. They might pay big dividends.

5/7

Forget your preconceived notions about professional success.

Quick! List some iconic tech entrepreneurs.

Let me guess. You thought of Zuckerberg. Jobs. Gates. Fadell.

Wait. You mean you haven’t heard of Tony Fadell, former CEO of Nest Labs, a company specializing in programmable thermostats?

Most people haven’t, actually. But that probably doesn’t bother Fadell, who sold Nest Labs to Google for the tidy sum of $3.2 billion.

What’s interesting about Fadell is that he doesn’t fit the popular image of a wildly successful tech entrepreneur. He’s not like Zuckerberg, Jobs, or Gates. Those three all shot to success when they were in their early 20s, after founding scrappy start-ups in their garages. None of them had much employment experience – instead, they earned reputations as renegades and rule-breakers, whose outsider status helped them succeed.

Not Fadell. He was in his early 40s when he founded Nest Labs. He wasn’t a rebel rule-breaker. He had an impressive CV, including stints at Phillips and Apple, which gave him the engineering know-how and managerial experience that helped make Nest Labs so successful. And he wasn’t an outsider, either. He recruited his team from a pre-existing network of peers and colleagues.

Fadell seems like the outlier here. Actually, he fits the profile of a successful founder better than any of the other three. See, the reason Zuckerberg and co capture our imagination is that their trajectories are so untypical. A study of 2.7 million entrepreneurs reveals that the median age for founders is 41.9. And, up until their 60s, older founders have the edge over their younger counterparts – the data shows they’re more likely to build sustainable, successful start-ups. So, to find entrepreneurial success, emulate Fadell: gain deep experience in a narrow field, and draw on your network when you strike out on your own.

Your gut might be telling you that because you’re not a 20-year-old tech whiz, you can’t be a successful founder. Well, remember Tony Fadell – and tell your gut to pipe down.

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Nature or nurture? The answer’s in the data.

New parents are expected to make, on average, 1,750 difficult decisions in the first year of their baby’s life. What should they call their bundle of joy? Breastfeeding or bottles? Cot or co-sleeping?

But research shows that these decisions might have very little to do with how a baby turns out. In other words: nature trumps nurture. Take the case of twins, Jim Lewis and Jim Springer who were raised separately from birth. When they reunited at age 39, both Jims were six-foot-tall and weighed 180 pounds. Both bit their nails and worked in law enforcement. Both had a childhood dog called Toy and both smoked Salem Lights.

There was one big difference. Jim Lewis called his son James Alan, while Jim Springer called his son James Allan – with two l’s.

One story doesn’t make a trend – but the data bears out the idea that most parenting choices aren’t make or break. Studies find that breastfed children enjoy no significant long-term health benefits than bottle-fed children. Children who are encouraged to play cognitively stimulating games like chess don’t, on average, grow up smarter than their peers. And children who are exposed to television don’t score worse on tests than those who aren’t.

Interestingly enough, there’s one area where a parental decision can significantly affect a child’s outcomes. And it’s got nothing to do with enrolling them in bassoon lessons or afternoon Latin classes.

The most impactful choice a parent can make for their child is where they choose to raise them. In the USA, simply moving to Seattle can boost your child’s projected future earnings by 11 percent. Not bad, right? But more important than choosing a specific city, is choosing the neighborhood where your kid grows up. Should you choose a neighborhood with a great school? Take on a big mortgage and move to a neighborhood with a high median income?

Not necessarily. The neighborhoods that a large study has found to be most advantageous for the children that grow up there all share three key traits. A high percentage of two-parent households, which tend to be stable. A high percentage of college graduates, who tend to be accomplished. And a high percentage of people who return their census forms, who tend to be engaged citizens.

Now, it doesn’t matter if you’re a single parent who never finished high school and tossed their census form in the trash by accident as long as you’re surrounded by other adults who embody these three traits. Why? Well, the data suggests that it’s not just parents that shape a child’s trajectory, but all the adults they routinely come into contact with. In fact, they might be more important than you. After all, your kids will probably want to rebel against you. They’ll likely have a much less complicated relationship with Mr. and Mrs. Suarez down the road, and therefore more happily accept them as role models.

It seems the data backs up the old African proverb: it takes a village to raise a child.

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Final summary

Follow your intuition. Trust your gut. Listen to your heart. Turns out all that old advice is, well, questionable. Often, the right decision is the one that seems risky or counter-intuitive. Luckily, it’s easy to uncover the best course of action, by relying on data insights. Time and again, from Wall Street to Silicon Valley and from romance to parenting, data-driven decision-making has been shown to yield results.