Don't Trust Your Gut: Using Data to Get What You Really Want in Life by Seth Stephens-Davidowitz

Don't Trust Your Gut: Using Data to Get What You Really Want in Life Reviews

• 
  Sheri

  I was both wary and weary of this book fairly early on. While there are interesting ideas to ponder, overall, the postulations are broad, apply only to very limited circumstances, and ignore other highly relevant factors, along with reason and rationale. I’m reminded to exercise my critical thinking and statistical literacy skills as statistics show what you want them to show; results are interpreted in terms of your own agenda. As I went on, I thought the book stayed on topic but drifted away from the main point the author wanted me to suppose, and this too factored into my low rating.
  
  This pop culture read may get you thinking and talking, but it's not a self-help book to improve your life or even scientifically influence your decision-making. There are no great revelations, and in the end, common sense prevails as well as the amount of effort you put into attaining your goals. Put yourself out there and you will achieve success through the wisdom that comes from hard work and determination.
• 
  Karen

  So conflicted about this book. So many interesting case studies and papers woven into this book, but written in an unappealing “tech-bro-come-data-scientist” style.
  
  What I found the most confusing, though, was the fact that as a book about using data, there was a blatant use of assumptions and full-on blanket statements that were clearly NOT data driven. For example, in the relationship chapter, the author writes (and this is word-for-word copied from the book), “Since Asian Men in the United States have above-average incomes, which tends to be attractive to women, the low response rates to their messages is even more striking.”
  
  Really, Seth? All Asian men in the States do? Or is it the men you encountered in your rarefied tech-bro-data-scientists circle? Or is it the Asian men from the study you quoted? Is there another way you could’ve written this so that you didn’t perpetuate the stereotype of Asians as the model minority?
  
  Anyway, these types of statements and assumptions are littered in this book and made it difficult to swallow, which is a pity, since a lot of the studies mentioned were fascinating.
• 
  Pete

  Don’t Trust Your Gut : Using Data to Get What You Really Want in Life (2022) by Seth Stephens-Davidowitz (SSD) is an interesting book where a data scientist uses the tools of his trade to look at how to improve our own lives. SSD has a PhD in economics and is a former Google data scientist so he’s ideally placed to write the book.
  
  There is a very good podcast interview with SSD by Steven Levitt on Levitt’s podcast ‘People I Mostly Admire’ for anyone pondering if they’d like to read the book or who has read it and would like to hear a little more about it all.
  
  The first Chapter looks at what data can be obtained on what makes a happy relationship and then becomes a look at what works on OKCupid in getting messages returned. What is always striking to me about the approach by those rated most attractive and least attractive is how much more successful women are at approaching men than the other way around. The least attractive tenth of females get a response 29% of the time while the most attractive men get a response 36% of the time. It’s curious that more women don’t ask men out. Perhaps Bumble is changing that. SSD points out that what this should lead to is keeping asking people out. Which is valuable advice but it does lead to the problem that many women have on dating site have with far too many poorly thought out messages. Perhaps many of us have met someone who is remarkably good with the opposite sex, then you watch what they do and see that lots of approaches does seem to be the key. The chapter goes on to look at what actually makes relationships work. A big part of it is finding someone who is happy with themselves.
  
  The book then looks at parenting and gets to the point that finding a good place where your kids find other good kids to grow up with is key. Also SSD points out the massive effect of genes on kids.
  
  The third chapter is a fun chapter that examines how to most easily get athletic success and in what sports are genes most important. Basketball, unsurprisingly due to the importance of height is one where genes makes a huge difference. SSD also finds the easiest sports to get some college scholarships in and the ones where making the Olympics is probably easiest.
  
  The very interesting Chapter Four examines how to get rich in America by looking at who is in top 0.1% of wealth and how they got there. It turns out owning a car dealership is a very good way to get rich. Alas SSD doesn’t mention how many car dealerships are inherited. Also choosing the top 0.1% is a bit arbitrary, perhaps being in top 5% might be useful. But it also might yield less interesting answers, like become a dentist, doctor, lawyer or engineer and save your money.
  
  Chapter Five looks at how to be successful as an entrepreneur or in business generally. SSD shows how really young founders are often less successful than older people. Indeed the average age for success seems to be someone in their 40s who understands a particular business and starts a company. Far less interesting that an early twenty something internet billionaire but far more likely.
  
  Then the book looks at how to hack luck and uses the example of AirBnB and how the two art school friends tried various things and kept changing what they did until they worked out how AirBnB could really work.
  
  Chapter Seven has fun with working out how to do a good makeover with data and lots of AI generated altered pictures of yourself.
  
  In Chapter Eight the book looks at what the data from happiness reporting apps says about how to be happy. It’s interesting. Getting off our couches, exercising and listening to music are reported as something that actually makes us happy. Sex is number one predictably. One thing this chapter didn’t explore is if people vary much in their answers.
• 
  Laura

  It is sad to see that eugenic myths continue to linger, even in the writing of a Jewish author, under the cover of Big Data science. The title is correct though: I shouldn't have trusted my gut when I thought this book would be a great read for me.
  
  Briefly, this publication is not a self-help book. It's more a collection of fun big-data summaries, very US-centric, about unintuitive realities. The book attempts to give advice on dating, changing your style, applying for jobs and building a business. But it mostly summarizes published results and endorses sometimes questionable data analysis--because oh, those dreadful controls are always hard to use in science.
  
  Some advice was honestly quite obvious: if you want to find a life partner, looks and status aren't predictors of future happiness; the neighborhood in which a child grows up, and the mentors surrounding them, predict future success. Then we take a deep dive into socio-genomics and try to resolve the age-old debate of nature vs nurture; using horribly controlled data the author is 100% certain he has the answer, and that is: genetics is all that matters. Oh, and the neighborhood in which your child is raised. There are much more intelligent books addressing the nature vs nurture debate, and why it's not just one or the other, so go read those instead. It was sad however to see a Jewish author so calmly say 'it's all about genetics'. Additionally, while the book acknowledges the existence of racism, it just...shrugs its shoulders at it. Oh, you want a successful kid? Just move to a good neighborhood. Let's not discuss zoning, racist practices when selling/buying houses for BiPOC people, ...Goodness there is so much to talk about here and the author just makes jokes about it and moves on!
  
  The only truly interesting part of the book for me was the one discussing success as an entrepreneur. The author used big data in a more convincing way (at least for me, I'm a biologist, not an economist, so perhaps the controls were off in this section as well), to argue that starting a successful business is dependent on age (the older you are the higher your chances of success) and experience (the more you have, the better your chances). But then we go down slippery slopes again, of discussing how your biggest chance of getting a job is applying far and wide (I agree there) and sending out a lot a lot alot of applications. That latter argument is, let's be honest, rather garbage. Improving your skills to write a cover letter/CV, and sending very targeted applications is likely to increase your chances of success much more than just sending applications out like a dummy. Sorry, but I've got experience in that field, and a lot of my friends who followed the authors advice took months to find a job. Targeted applicants had a much higher rate of success and got jobs much faster. Other than the argument that if you fail, try-try again, not much else is of value in this section.
  
  I abandoned the book when we entered the 'what makes us happy' chapter and the author started talking about sex making people happy and him having sex with his partner. After a big EWWW (and I'm not prudish) I've decided to trust my gut--telling me I'm wasting my time and I've got other books to read--and didn't listen to the last hour of the audiobook.
  
  I don't recommend.
  
  PS: I never liked Seinfeld, and it's over use in this book was annoying. Since the author apparently likes to read his reviews: please consider diversifying your examples in future books. For other readers' sake, because I've been put off by this current publication and won't be sampling future works by this author.
• 
  Bryan Vandevoorde

  Less captivating/revolutionary in terms of topics covered as compared to 'Everybody Lies', but written in Seth's same greatly compelling style.
• 
  Chris Boutté

  I’ve read over 90 books in 2022, and this book is definitely in my top 5. Maybe even top 3. I absolutely loved Seth’s previous book Everybody Lies and had no clue he was working on a new book, so this was a pleasant surprise. For those who are unfamiliar with Seth’s work, he dives into data to debunk a lot of conventional wisdom and help us see the truth that’s often hidden by our biases and other cognitive shortcuts. His previous book was more about helping us see the world through a clearer lens, but this one is much different.
  
  Seth brands this book as self-help, but I don’t even think that category does the book justice because it covers so much. Yes, if you read this book, I guarantee your life will get at least a tiny bit better if you take in what he’s showing with the data, but it also helps us take a look at how we all have different advantages and disadvantages. The book covers what the data really says about what makes for long-lasting, great relationships, the biggest factor when it comes to children becoming successful, and how Seth gave himself a makeover using data to make himself more attractive.
  
  Since I’m obsessed with the topics of skill, success, and luck, my favorite two chapters covered these topics. A lot of books either don’t cover both sides of this debate or don’t do a great job of doing so. I think Seth nailed it by discussing how luck plays a big role in success, but it also takes hard work and taking advantage of opportunities that come your way.
  
  I could talk about this book all day, but this is all I’ll say for now. Hopefully, it has interested you enough to go get a copy ASAP.
  
• 
  Ryan

  Hybrid "data science applied to populations" and "self-help" book; interesting concept, a few decent insights (but most fairly obvious, or reported widely elsewhere). Great if you have zero exposure to behavioral economics or any of that research; entertaining but kind of pointless if you're familiar with the research. (The "interesting" insights are from the research showing humans care about "ending pain" of an experience, and are largely insensitive to duration; that iPhones are a great data gathering tool for experiences; humans enjoy spending time with humans they select/like, and like being in nature, and dislike doing unpleasant tasks.). It's nice that this stuff is data supported but nothing which is particularly life changing and non-obvious for most people.
• 
  Ben Rogers

  Another Banger By SSD
  
  This was an outstanding book.
  
  I absolutely LOVED his first book,
  Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are, which at the writing of this review remains as one of my top 24 books I have ever read.
  
  This book focused on similar information as the first book, but went into much more detail.
  
  I found each chapter endlessly fascinating.
  
  From learning about what neighborhoods to live in to optimize your children's development, to how to optimize your online dating, to even your own physical appearance - Seth takes a data-driven, data-first approach on every decision.
  
  I loved the little details and quips throughout the book.
  
  I also enjoyed the common misconceptions bits. A few of them I did know (like the one on IQ) but I found them really interesting.
  
  My favorite chapter I would have to say was on hacking luck, although, I can't say that I am a big fan of Airbnb, as they decimated the housing unaffordability here in Vancouver from parasites owning multiple properties which mostly sit empty.
  
  I was also fascinated by the dating chapter.
  
  Another chapter that can really change your life is the one on experiences and enjoyment activities. I definitely got a lot out of that one!
  
  Another excellent book.
  
  I look forward to his next!
  
  Check this out!
  
  4.5/5
• 
  Thomas

  As someone educated in data and social sciences, this book immediately caught my eye. It looks at many areas of life that are rather complex (e.g., what makes people happy, what makes people attracted to you, what parenting decisions lead to the best outcomes) and brings data into the conversation to shed light on how to improve yourself and your life in these seemingly complex areas. While it was an entertaining read with a humorous writing style, many of the studies used to make a point had flawed methods and made it difficult for me to take the points that were being made seriously. Ultimately the areas being looked at are highly personalized and too complex to be simplified into looking at one study, with questionable methods. The author does acknowledge this in some areas, such as with what makes for a lasting relationship. I would give this one 2.5/5 stars but rounded up to 3 due to the entertaining writing style.
• 
  Dan

  I barely got through the introduction. He said the words big data many times. He also said that big data could answer some of our most pressing questions. Somehow the author thought one of those pressing questions was the probability of becoming a celebrity or why some people are lucky. I persevered and read the first chapter about parenting. It was not good. For instance, evidently big data can tell you the best places in the country to raise a child. Big data says that Reading Pennsylvania is one of the top five places. Evidently big data hasn't visited Reading, nor has the author I guess. Granted that result is from a very well-respected researcher who I happen to admire, so I may be cherry picking a bit. Speaking of cherry picking, this book is filled with it. The author said that big data told him that self-help books for popular and therefore he chose to write a book that was in the self-help category. Here is some self-help advice, don't read this book.
• 
  Scott Sharp

  This could have been 15 pages of findings with an equal number of citations, endnotes, and definitions.
• 
  Daniel

  One of the best books I ever read.
  
  1. Parenting: best methods only raise kids’ income by 26%. So chill about the 1000 decisions. Most important: place. Check IRS data study
  2. Marriage happiness: main correlation is person is happy and has a growth mindset. Looks don’t matter much. Look for Samantha Joel’s big data research
  3. Dating: good looks, tall men, short women
  4. Looks: help with everything. Try different looks with app and poll your friends or follower
  5. Rich: to be rich you must own a successful business. Most successful entrepreneurs are older and excellent in their field and then open their own shop. Mark Zuckerbergs are rare. Join fields like alcohol distribution business where regulation prevents competition.
  6. Success: successful firms have similar lucky breaks but capitalise on them. Successful artists have more output and show them at many more places so they have a higher chance to stumble upon a lucky break.
  7. Happiness: nature makes us happy. Sex, fishing, hunting, gardening, hiking and exercise are all great. Watching TV, playing video games and checking social media makes us miserable. Friends make us happy (but not acquaintances). Work sucks unless you work with friends. Check out Mappiness research.
• 
  Ryan

  In Don't Trust Your Gut, Seth Stephens-Davidowitz argues that we should all "moneyball" our lives.
  
  I'm not sure whether I'm a great reader for this book. I loved Moneyball, I like analytics based commentary on sports like Zach Lowe's early work at Grantland, and I like wonky writing like Nate Silver's 538 articles or Matthew Yglesias' substack. I also liked SS-D's previous book, Everybody Lies. But I also loathe the "always be optimizing" ethos that I associate with Justin Timberlake's character in The Social Network. Somehow, this ethos seems to revel in its lack of substance. I suppose I am more apt to trust David Brooks' solemn advice that we build character by wrestling with our "signature sins."
  
  There's also something in the tone of books like Freakonomics, Predictably Irrational, and Don't Trust Your Gut that sets my teeth on edge. SS-D's conclusion reads: "The data-driven answer to life is as follows: be with your love, on an 80-degree and sunny day, overlooking a beautiful body of water, having sex." Sorry, but that's too "bro" for me.
  
  I also dislike how SS-D quotes from authoritative sources. Here's the first line of chapter 1:

  'Whom should you marry? This may be the most consequential decision of a person's life. The billionaire investor Warren Buffett certainly thinks so. He calls whom you marry "the most important decision that you make."'

  First, how much scholarship went into this beyond looking up marriage on brainyquote? Second, a better paragraph might read: 'The billionaire investor Warren Buffett says whom you marry is "the most important decision that you make." He's right.' But I doubt we need the quote from Buffett at all as it offers no insight beyond being a claim made by a rich person.
  
  The most genuine part of Don't Trust Your Gut might have been when SS-D admits to losing nearly a decade of his life to depression, but almost nothing in this self-help book speaks to that process. There is one mention of a therapist, but perhaps therapy is more productive when it's less about optimization. Or maybe it's just OK to admit one feels melancholy sometimes.
• 
  Francisco Galán

  What an amazing book, full of interesting ideas, and backed-up by evidence and data. This guy is an absolute legend; he won my heart in the chapter where he tried to improve his appearance using data and statistical analyses.
  
  Oh, and the writing style is a delight: concise, simple, and extremely funny, in a way I've never seen a non-fiction book be funny. Highly recommended!
• 
  Peyton Gibson

  Like Freakonomics, I think this is a good book to get folks who may not have been inclined towards data science to learn about its potential. It is easy and fun to read. However, I think the author repeats many of the same mistakes as Freakonomics-- extrapolating and overgeneralizing data/results and pronouncing his findings to be profound. Like many data scientists and economists, he leaves out important context and attempts to condense massive fields into study (e.g., happiness and how where you grow up matters), into short, quirky "life hacks". He does admit shortcomings of some of the results/studies occasionally, but I often found myself skeptical and noting potential omitted variables and reverse causalities. Like many of the books in this category, I also found that there was not nearly enough (if any at all?) consideration or discussion of folks without the same privileges as the author-- especially since the book is likely geared towards wealthier, educated individuals, it's important to take a step back and understand how the suggested "life hacks" in this book do not apply to a lot of people.
• 
  Jed Walker

  Really interesting data from a wide range of studies.
  Really terrible analysis & application of that data.
• 
  Sarah Whitten Losada

  In a world of abstract self-help vaguely backed up by a few cherry-picked studies, it's SO refreshing to see somebody lead with the data.
  
  Advice not laced with ideology is hard to come by. This book does a great job of giving the sometimes not-so-sexy answers to some of life's biggest questions.
  
  It's essentially a meta-analysis on how to live life, which could easily render itself to sterile science, but he keeps this book light with engaging story-telling and lots of self-deprecating humor. What a combo. Excellent read.
• 
  Michael

  Seth Stephens-Davidowitz did it again. "Don't Trust Your Gut" is a captivating read—revealing insights about our lives that we might not have otherwise known—or that we may have known but don't know that we know (don't ask me, read the book).
  
  Reading this book will make you happy. Not, like, super happy, but happier than many other activities. Don't believe me? Well, then, you definitely need to read the book.
• 
  Jung

  ** DATA IS LOVE **
  
  Don’t Trust Your Gut (2022) turns that tried-and-true wisdom about trusting your gut on its head. Not only does trusting your gut instinct often lead you to make the wrong decision, there’s a pretty foolproof method to ensure you make the right decision – analyzing the available data and acting on it.
  
  Seth Stephens-Davidowitz is a contributing op-ed writer for the New York Times, a lecturer at The Wharton School, and a former Google data scientist. He received a BA from Stanford and a PhD from Harvard. His research has appeared in the Journal of Public Economics and other prestigious publications. He lives in New York City.
  
  FULL SCRIBE -
  https://shrib.com/#GilaMonster8OQ3NW0
  
  ---
  
  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 Don’t Trust Your Gut, you just might be.
  
  ---
  
  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.
  
  ---
  
  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.
  
  ---
  
  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.
  
  ---
  
  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.
  
  ---
  
  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.
  
  ---
  
  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.
• 
  Jaan Kokk

  A book that illustrates through multiple examples ways in which data analysis and quantitative measures can be brought towards understanding complex fundamental issues. Some of the examples do leave a considerable amount of room for criticism, but aside from that, the book does a good job of providing perspective and ideas on how to approach complex questions.
• 
  Harshan Ramadass

  Nicely written. Finished bulk of the book in single sitting, that good! The guy may have broad forehead and may not kick the football far enough to draft, but he sure can write an entertaining book using clever data. Probably driven enough to show up at lots of places, and in a monopolistic market of book writing to be amongst top 1% or even 0.5% of American earners! Waiting to read his next memoir, followed by a book about how sexed up he is :)
• 
  Max McKinnon

  I found Everybody Lies, his previous book on google trends, powerfully insightful, and I was expecting a similar caliber book going into this one. The opening premise “data driven self help”, tone, and comedy of this book I loved, but after that it didn’t deliver.
  
  I especially disliked the chapters on data driven dating. This book is almost a case study in where you can be right in a bubble but also so wrong in the greater context. It’s the age old classic of the right answer to the wrong question.
  
  The data definitively shows that doing things like wearing glasses, taking the same exact pictures but with better lighting and with better hairstyles will improve a person’s results on dating websites. Great. But fairly useless compared to the potential gains by instead setting up opportunities to meet and talk to women in person.
  
  Would have been nice to see the data on people who skipped dating websites. Data is easy to come by on dating websites, but the results are a bubble subset that don’t explain the real dating world.
  
  The chapter on making your own luck as an entrepreneur was pretty good. The take away was going to multiple venues and building a bigger audience can be more successful to hitting a lucky break than doing the same show in the same town every week. Kind of ironic to get it right here. That’s the advice he should have given for dating as it’s essentially the same thing.
  
  The chapter on parenting I found extremely interesting. The results were cleverly obtained by looking at twins data. It showed that by large the location in which you raise your kids is the most important factor to their financial salary later in life. Essentially what this means is that you don’t have as much control over your child’s life as you think you do as a parent. However I disagreed with his conclusion that parents should stop trying so hard. For things like stressing about nothing, sure. But for other priorities as a parent, I’m pretty sure it’s very important. There’s a strong correlation between no father in the house/community and prison for one example. Or for another example, 70% of Americans are overweight or obese. As a parent you have direct control over how your child forms eating habits during the first few years. There’s a big difference between nutritious habits and empty calorie habits. Not to mention all the mental health issues that are plaguing the country, most of which can be traced back to emotionally inadequate or emotionally abusive parenting. I am skeptical of those results to be honest, and I don’t think financial salary is the best way to measure a child’s future success but nonetheless it was an interesting insight that I agree with, that the community (that you can choose as a parent) has a huge effect on the child’s future.
  
  The sports chapter was interesting and another example of how to be right in a bubble while at the same time so wrong in a wider context. The data showed that the best sport to get into in terms of scholarship potential is the equestrian team. But this is assuming the only reason you’re getting into sports is to get a scholarship, not your enjoyment of the sport. If your entire game is to get a scholarship through sports regardless of whether you actually like the sport then why arbitrarily limit yourself to sports? Sports are not even close to as profitable as science fair scholarships in terms of hours in versus payoff. Again the data answers the question, but he’s asking the wrong question.
  
  The mappiness surveys chapter on what makes people happy was interesting. Yet again it was interesting but didn’t generalize to a wider context. Also the idea of basing personal passions off of an external algorithm is so ironic. How can someone be happy doing exactly what the model thinks they should do to be happy? How boring is that? This advice is not too different from advising to scroll social media feeds because they know what you want next. They do. It doesn’t mean it’s what you really want next though.
  
  I really enjoyed parts of this book and would give certain parts five out of five. Other parts of the book were worthless or even harmful. I can’t stop laughing about the dating advice to a bunch of fools optimizing their tinder profiles and complaining about male to female ratios all the while being too scared to talk to women they encounter every day 😂.
  
  Overall the book is not in the same must read caliber as the author’s previous work Everybody Lies. It wasn’t bad either, on par with a typical Gladwell book but with less cohesive narrative.
• 
  Steven

  Love a book that accomplishes 0% of what its title and subtitle suggest.
  
  In the lovely world of self-help dreck, men market their opinions on how to make your life better. This book isn't even that. It's a slop of studies that suggest the best choice of what to do in the realms of sports (go for the one where you're more likely to get a scholarship), raising a child (pick a good neighborhood with lots of role models), what kind of real estate you should do (DON'T own a record store), whether or not to use social media on your phone (no, obviously)...
  
  I picked up this book because I wanted someone's attempt at an "empirical" view of human life as it stands right now. Instead I got something silly and messy and mostly useless, and the last sentence might be one of the worst things I've read in any book:
  
  "The data driven answer to life is as follows: be with your love, on an 80-degree and sunny day, overlooking a beautiful body of water, having sex."
  
  I think the only useful thing this book offered was that birdwatching is an underrated happiness activity. Might give it a try sometime.