Think Like a Rocket Scientist: Simple Strategies You Can Use to Make Giant Leaps in Work and Life

by Ozan Varol

To think like a rocket scientist is to look at the world through a different lens. Rocket scientists imagine the unimaginable and solve the unsolvable. They transform failures into triumphs and constraints into advantages. They view mishaps as solvable puzzles rather than insurmountable roadblocks. They’re moved not by blind conviction but by self-doubt; their goal is not short-term results but long-term breakthroughs. They know that the rules aren’t set in stone, the default can be altered, and a new path can be forged.

Companies fail because they stare at the rearview mirror and keep calling the same plays from the same playbook. Instead of risking failure, they stick with the status quo. In our daily lives, we fail to exercise our critical-thinking muscles and instead leave it to others to draw conclusions.

Genius hesitates. —CARLO ROVELLI

In the modern world, we look for certainty in uncertain places. We search for order in chaos, the right answer in ambiguity, and conviction in complexity. “We spend far more time and effort on trying to control the world,” Yuval Noah Harari writes, “than on trying to understand it.”11 We look for the step-by-step formula, the shortcut, the hack—the right bag of peanuts. Over time, we lose our ability to interact with the unknown.

Our approach reminds me of the classic story of the drunk man searching for his keys under a street lamp at night. He knows he lost his keys somewhere on the dark side of the street but looks for them underneath the lamp, because that’s where the light is. Our yearning for certainty leads us to pursue seemingly safe solutions—by looking for our keys under street lamps.

Einstein described his own discovery process in similar terms: “Our final results appear almost self-evident,” he said, “but the years of searching in the dark for a truth that one feels, but cannot express; the intense desire and the alternations of confidence and misgiving, until one breaks through to clarity and understanding, are only known to him who has himself experienced them.”14 In some cases, scientists keep stumbling around in the dark room, and the search continues well past their lifetime. Even when they find the light switch, it may illuminate only part of the room, revealing that the remainder is far bigger—and far darker—than they imagined. But to them, stumbling around in the dark is far more interesting than sitting outside in well-lit corridors.

Our ability to make the most out of uncertainty is what creates the most potential value. We should be fueled not by a desire for a quick catharsis but by intrigue. Where certainty ends, progress begins.

“There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know.”16 These remarks were widely ridiculed—in part because of their controversial source—but as far as political statements go, they’re surprisingly accurate. In his autobiography, Known and Unknown, Rumsfeld acknowledges that he first heard the terms from NASA administrator William Graham.17 But Rumsfeld conspicuously omitted one category from his speech—unknown knowns.

“The great obstacle to discovering,” historian Daniel J. Boorstin writes, “was not ignorance but the illusion of knowledge.”

Anomalies distort this clean picture of good and bad and right and wrong. Life is taxing enough without uncertainty, so we eliminate the uncertainty by ignoring the anomaly. We convince ourselves the anomaly must be an extreme outlier or a measurement error, so we pretend it doesn’t exist. This attitude comes at a huge cost. “Discovery comes not when something goes right,” physicist and philosopher Thomas Kuhn explains, “but when something is awry, a novelty that runs counter to what was expected.”49 Asimov famously disputed that “Eureka!” is the most exciting phrase in science. Rather, he observed, scientific development often begins by someone noticing an anomaly and saying, “That’s funny…”50 The discovery of quantum mechanics, X-rays, DNA, oxygen, penicillin, and others, all occurred when the scientists embraced, rather than disregarded, anomalies.

The status quo is a super magnet. People are biased against the way things could be and find comfort in the way things are. If you had any doubts about our obsession with the status quo, take a look at all these idioms we’ve dedicated to avoiding change: “If it ain’t broke, don’t fix it.” “Don’t rock the boat.” “Don’t change horses in the middle of the stream.” “Go with the devil you know.” The default carries immense power, even in advanced industries like rocket science. This idea is called path dependence: What we’ve done before shapes what we do next.

The width of the engines that powered the space shuttle—one of the most complex machines humankind has ever created—was determined over two thousand years ago by a Roman road engineer.3 Yes, you read that correctly. The engines were 4 feet 8.5 inches wide because that was the width of the rail line that would carry them from Utah to Florida. The width of that rail line, in turn, was based on the width of tramlines in England. The width of the tramlines, in turn, was based on the width of the roads built by the Romans: 4 feet 8.5 inches.

We treat our processes and routines like roads collecting traffic. A 2011 survey of more than a hundred American and European companies shows that “over the past 15 years, the amount of procedures, vertical layers, interface structures, coordination bodies, and decision approvals needed in each of those firms has increased by anywhere from 50% to 350%.”6 Here’s the problem. Process, by definition, is backward looking. It was developed in response to yesterday’s troubles. If we treat it like a sacred pact—if we don’t question it—process can impede forward movement. Over time, our organizational arteries get clogged with outdated procedures.

The credit for first-principles thinking goes to Aristotle, who defined it as “the first basis from which a thing is known.”12 The French philosopher and scientist René Descartes described it as systematically doubting everything you can possibly doubt, until you’re left with unquestionable truths.13 Instead of regarding the status quo as an absolute, you take a machete to it. Instead of letting your original vision—or the visions of others—shape the path forward, you abandon all allegiances to them. You hack through existing assumptions as if you’re hacking through a jungle until you’re left with the fundamental components. Everything else is negotiable.

But for the space shuttle, reusability was neither quick nor complete. The cost of inspection and refurbishment was outrageously high, particularly given the infrequency of shuttle flights. The turnaround required “more than 1.2 million different procedures,” taking months and costing more than a new space shuttle.18 If you reason by analogy, you would conclude that reusable spacecraft are a bad idea. It didn’t work for NASA, so it won’t work for us. But this reasoning is flawed. The case against reusability was built on a single case study: the space shuttle. The problem, however, was with the shuttle itself, not with all reusable spacecraft.

To mop the mist collected on your mental windshield in those areas and expose the invisible rules governing your life, spend a day questioning your assumptions. With each commitment, each presumption, each budget item, ask yourself, What if this weren’t true? Why am I doing it this way? Can I get rid of this or replace it with something better?

This popular description happens to be wrong. Occam’s razor is a guiding principle—not a hard-and-fast rule. Nor is it a preference for the simple at all costs. Rather, it’s a preference for the simple, all other things being equal. Carl Sagan put it well: “When faced with two hypotheses that explain the data equally well,” you should “choose the simpler.”37 In other words, “when you hear hoofbeats, think horses, not unicorns.”

Simplicity also reduces costs. The Atlas V rocket—which has taken many objects, including military satellites and Mars rovers, into space—uses up to three types of engines for different stages of flight.42 This complexity drives up the expenses: “To a first-order approximation,” Musk explains, “you’ve just tripled your factory costs and all your operational costs.” In contrast, SpaceX’s Falcon 9 has two stages with the same diameter and the same engines built from the same aluminum-lithium alloy. This simplicity enables high-volume production at a lower cost, while boosting reliability. What’s more, unlike other aerospace companies that build their vehicles vertically—in the same position that they’re launched—SpaceX assembles them horizontally.43 This orientation allows the company to use a regular warehouse, eliminating the need to build a skyscraper—not to mention the safety issues that come with workers dangling sixty feet in the air while building a rocket. “Every decision we’ve made,” Musk says, “has been with consideration to simplicity.… If you’ve got fewer components, that’s fewer components to go wrong and fewer components to buy.”

Leonardo da Vinci did the same. He famously used his notebooks for thought experiments, sketching various engineering designs he formulated in his mind—from flying machines to churches—instead of physically constructing them.7 Let’s pause there for a moment. As shocking as it sounds, we can generate breakthroughs simply by thinking. No Google. No self-help books. No focus groups or surveys. No advice from a self-proclaimed life coach or an expensive consultant. No copying from competitors. This external search for answers impedes first-principles thinking by focusing our attention on how things are rather than how they could be. Thought experiments take this external inquiry and turn it inward—just you and your imagination. “Pure thought,” Einstein said, “can grasp reality.”8 Thoughts can disprove an argument, show why something will or won’t work, and illuminate the way forward—all without a single physical experiment.

These epiphanies appear effortless, but they’re the product of a long, slow burn. A breakthrough begins with asking a good question, laboring over the answer intensely, and being stuck in idleness for days, weeks, and sometimes years. Research shows that incubation periods—the time you spend feeling stuck—boosts the ability to solve problems.

When our ancestors blazed a trail to some unknown corner of the earth, they took a moonshot. The discoverers of fire, the inventors of the wheel, the builders of the pyramids, the makers of automobiles—they all took moonshots. It was a moonshot for slaves to reach for freedom, for women to take the ballot, and for refugees to push toward distant shores in search of a better life. We’re a species of moonshots—though we’ve largely forgotten it. Moonshots force you to reason from first principles. If your goal is 1 percent improvement, you can work within the status quo. But if your goal is to improve tenfold, the status quo has to go. Pursuing a moonshot puts you in a different league—and often an entirely different game—from that of your competitors, making the established plays and routines largely irrelevant.

“The story of the human race,” psychologist Abraham Maslow wrote in 1933, “is the story of men and women selling themselves short.”13 If Kennedy were following this mindset, his speech would have been very different (and far more boring). “We choose,” he might have said, “to put humans in Earth orbit and make them circle round and round—not because it’s challenging—but because it’s doable given what we have.” (Which, incidentally, is exactly what NASA decided to do in the 1980s. More on that later.)

Divergent thinking is a way of generating different ideas in an open-minded and free-flowing manner—like flies bouncing around in a glass bottle. During divergent thinking, we don’t think about constraints, possibilities, or budgets. We just throw around ideas, open to whatever might present itself. We become optimists in the way that physicist David Deutsch defines the term—as someone who believes that anything permitted by the laws of physics is doable.25 The goal is to create a flurry of options—both good and bad—not prematurely judging them, limiting them, or choosing among them. At the initial stages of idea formation, “the pure rationalist has no place,” as the physicist Max Planck put it. Discovery, as Einstein also explained, “is not a work for logical thought, even if the final product is bound in logical form.”26 To activate divergent thinking, you must shut down the rational thinker in you, the part responsible for otherwise safe, beneficial grown-up behaviors. Set aside the spreadsheets, and let your brain run wild. Investigate the absurd. Reach beyond your grasp. Blur the line between fantasy and reality.

Consider a study by three Harvard Business School professors who gave the participants a difficult ethical challenge.28 The researchers laid out a scenario where the ethical choice wasn’t obvious and divided the study’s participants into groups. To one group, they asked, “What should you do?” To the other group, they asked, “What could you do?” The “should” group zeroed in on the most obvious solutions—often not the best ones—but the “could” group stayed open-minded and generated a broader range of possible approaches. As the researchers explained, “People may often benefit from a could mindset that involves a more expansive exploration of possible solutions before making a final decision.” A different study reached the same conclusion. Participants who were told “Object A could be a dog’s chew toy” as opposed to “Object A is a dog’s chew toy” generated a greater variety of uses for the toy.

For most of us, planning for the future means forecasting. In our businesses, we review the current supply and demand for widgets and extrapolate them into the future. In our personal lives, we let our current skill set drive our vision for who we might become. But forecasting, by definition, doesn’t start with first principles. With forecasting, we look in the rearview mirror and at the raw materials in front of us, rather than the possibilities ahead. When we forecast, we ask, “What can we do with what we have?” Often, the status quo itself is part of the problem. Forecasting takes all our problematic assumptions and biases and propels them into the future. In so doing, it artificially restricts our vision of what is feasible, given the current circumstances. Backcasting flips the script. Rather than forecasting the future, backcasting aims to determine how an imagined future can be attained. “The best way to predict the future,” Alan Kay says, “is to invent it.”83 Instead of letting our resources drive our vision, backcasting lets our vision drive the resources. When we backcast, we take our bold ambition and introduce actionable steps. We visualize our ideal job and sketch out a roadmap to get there. We picture the perfect product and ask what it takes to build it. Only when you face the real prospect of sketching a blueprint for success—now, not later—will you be forced to separate fact from fiction.

But here’s the problem: Building the pedestal is the easiest part. “You can always build the pedestal,” Teller says. “All of the risk and the learning comes from the extremely hard work of first training the monkey.”87 If the project has an Achilles heel—if the monkey can’t be trained to talk, let alone recite Shakespeare—you want to know that up front. What’s more, the more time you spend building the pedestal, the harder it becomes to walk away from moonshots that shouldn’t be pursued. This is called the sunk-cost fallacy. Humans are irrationally attached to their investments. The more we invest time, effort, or money, the harder it becomes to change course. We continue to read a terrible book because we already spent an hour reading the first few chapters or pursue a dysfunctional relationship because it has dragged on for eight months. To counter the sunk-cost fallacy, put the monkey first—tackle the hardest part of the moonshot up front. Beginning with the monkey ensures that your moonshot has a good chance of becoming viable before you’ve poured massive amounts of resources into a project.

Initially, we asked the obvious questions: How can we innovate on the flawed design of the Mars Polar Lander? How do we design a better three-legged lander to ensure a smooth landing? But these questions, as we’ll discover, weren’t the right questions to be asking. This chapter examines the importance of searching for a better question instead of a better answer. In the first part of this book (“Launch”), you learned how to reason from first principles and ignite your thinking by conducting thought experiments and taking moonshots to generate radical solutions to thorny problems. But often, the question we originally conceived isn’t the best one to ask, and the first problem we identified isn’t the best one to tackle.

The way that most people solve problems reminds me of a scene from Alice’s Adventures in Wonderland. In the scene, the Knave of Hearts is on trial for supposedly stealing tarts. After the evidence is presented, the King of Hearts, who’s presiding as a judge, says, “Let the jury consider their verdict.” The impatient Queen of Hearts interrupts and retorts, “No, no! Sentence first. Verdict afterwards.” In solving problems, we instinctively want to identify answers. Instead of generating cautious hypotheses, we offer bold conclusions. Instead of acknowledging that problems have multiple causes, we stick with the first cause that pops to mind. Doctors assume they have the right diagnosis, which they base on symptoms they have seen in the past. In boardrooms across America, executives, eager to appear decisive, fall over each other to be the first to deliver the correct answer to a perceived problem. But this approach puts the cart before the horse—or the sentence before the verdict. When we immediately launch into answer mode, we end up chasing the wrong problem. When we rush to identify solutions—when we fall in love with our diagnosis—our initial answer hides better ones lurking in plain sight. When the sentence is announced first, the verdict is always the same: guilty. The difficulty lies, as John Maynard Keynes said, “not in the new ideas, but in escaping from the old ones.”

The result was the Embrace infant warmer. It’s a small, light sleeping bag that wraps around the infant. A pouch of phase-change material—which is an innovative wax—keeps the baby at the right temperature for up to four hours. You can “recharge” the warmer in only a few minutes by putting it in boiling water. And compared with the $20,000 to $40,000 price tag of a traditional incubator, the Embrace costs only $25. By 2019, the cheap and reliable product has embraced hundreds of thousands of premature infants in over twenty countries. Often, we fall in love with our favorite solution and then define the problem as the absence of that solution. “The problem is, we need a better three-legged lander.” “The problem is, we don’t have enough incubators.” In each case, we pursue technology for the sake of technology. We lose the forest for the trees, the purpose for the method, the function for the form. This approach mistakes tactics for strategy. Although the terms are often used interchangeably, they refer to different concepts. A strategy is a plan for achieving an objective. Tactics, in contrast, are the actions you take to implement the strategy. We often lose sight of the strategy, fixate on the tactics and the tools, and become dependent on them. But tools, as author Neil Gaiman reminds us, “can be the subtlest of traps.”33 Just because a hammer is sitting in front of you doesn’t mean it’s the right tool for the job. Only when you zoom out and determine the broader strateg...

But the team that made the most money approached the problem differently. The students understood that both the five-dollar funding and the two-hour period weren’t the most valuable assets at their disposal. Rather, the most valuable resource was the three-minute presentation time they had in front of a captive Stanford class. They sold their three-minute slot to a company interested in recruiting Stanford students and walked away with $650. What is the five-dollar tactic in your own life? How can you ignore it and find the two-hour window? Or even better, how do you find the most valuable three minutes in your arsenal?

In both cases, Amazon looked beyond the function to the form. The function of Whole Foods stores was to sell groceries, but the stores took the form of a massive real estate footprint with storage and refrigeration that could be repurposed for distribution. The function of Amazon’s computing infrastructure was for internal support, but its form—a massive data center—could provide a highly profitable service to companies such as Netflix and Airbnb.

Frank McClure was also thunderstruck by Sputnik, but for a different reason. McClure was then the deputy director at the Applied Physics Laboratory. He called Guier and Weiffenbach into his office and asked them a simple question: “Can you guys do the reverse?” If the two men could calculate the unknown trajectory of a satellite from a known location on Earth, could they find an unknown location on Earth using the known location of a satellite? This question may sound like a theoretical riddle, but McClure had a very practical application in mind. At the time, the military was developing nuclear missiles capable of being launched from submarines. But there was a problem. To strike a precise location with a nuclear missile, the military had to know the precise location of the launch site. In the case of nuclear submarines swimming through the Pacific Ocean, their precise location was unknown. Hence the question: Can you discover the unknown location of our submarines through the known location of a satellite we’ll launch into space? The answer was a resounding yes. It took only three years after the launch of Sputnik for the United States to implement this thought experiment and launch five satellites into orbit to guide its nuclear submarines. Although it was called the Transit system at the time, its name was changed in the 1980s to something that has become an everyday term: the global positioning system, or GPS.

THE NEXT TIME you’re tempted to engage in problem solving, try problem finding instead. Ask yourself, Am I asking the right question? If I changed my perspective, how would the problem change? How can I frame the question in terms of strategy, instead of tactics? How do I flip the thumbtack box and view this resource in terms of its form, not its function? What if we did the reverse? Breakthroughs, contrary to popular wisdom, don’t begin with a smart answer. They begin with a smart question.

We’ll explore the benefits of switching our default from convincing others that we’re right to convincing ourselves that we’re wrong.

As a former scientist, I’ve been trained to rely on objective facts. For years, when I was attempting to persuade someone, I would back my arguments with hard, cold, irrefutable data and expect immediate results. Drowning the other person with facts, I assumed, was the best way to prove that climate change is real, the war on drugs has failed, or the current business strategy adopted by your risk-averse boss with zero imagination isn’t working. But I’ve discovered a significant problem with this approach. It doesn’t work.

When we seclude ourselves from opposing arguments, our opinions solidify, and it becomes increasingly harder to disrupt our established patterns of thinking. Aggressively mediocre corporate managers remain employed because we interpret the evidence to confirm the accuracy of our initial hiring decision. Doctors continue to preach the ills of dietary cholesterol despite research to the contrary. University students maintain their beliefs even when those beliefs violate the laws of physics. Recall that it was Galileo who discovered, through a thought experiment, that objects of different masses fall at the same rate in a vacuum. In one study, university students were asked whether they thought heavier objects fell faster than lighter ones.6 After they recorded their answers, the students then observed a physical demonstration where a metal and a plastic of the same size were dropped from the same height in a vacuum. Although the two objects fell at the same rate, the students who initially believed the heavier metal would fall faster were more likely to report that the metal did fall faster.

Opinions are sticky. Once we form an opinion—our own very clever idea—we tend to fall in love with it, particularly when we declare it in public through an actual or a virtual megaphone. To avoid changing our mind, we’ll twist ourselves into positions that even seasoned yogis can’t hold. Over time, our beliefs begin to blend into our identity. Your belief in CrossFit makes you a CrossFitter, your belief in climate change makes you an environmentalist, and your belief in primal eating makes you paleo. When your beliefs and your identity are one and the same, changing your mind means changing your identity—which is why disagreements often turn into existential death matches. As a result, at the outset of their investigation, scientists refrain from stating opinions. Instead, they form what’s called a working hypothesis. The operative word is working. Working means it’s a work in progress. Working means it’s less than final. Working means the hypothesis can be changed or abandoned, depending on the facts. Opinions are defended, but working hypotheses are tested. The test is performed, as geologist and educator T. C. Chamberlin explains, “not for the sake of the hypothesis, but for the sake of facts.”14 Some hypotheses mature into theories, but many others don’t.

When we start with a single hypothesis and run with the first idea that pops into mind, it’s much easier for that hypothesis to become our master. It anchors us and blinds us to alternatives sitting in the periphery. As author Robertson Davies put it, “The eye sees only what the mind is prepared to comprehend.”23 If the mind anticipates a single answer—the Mars Polar Lander may be alive—that’s what the eye will see. Before announcing a working hypothesis, ask yourself, what are my preconceptions? What do I believe to be true? Also ask, do I really want this particular hypothesis to be true? If so, be careful. Be very careful. Much as in life, if you like someone, you’ll tend to overlook their flaws. You’ll find signals from a love interest—or a spacecraft—even when they’re not sending any. To make sure you don’t fall in love with a single hypothesis, generate several. When you’ve got multiple hypotheses, you reduce your attachment to any one of them and make it more difficult to quickly settle on one. With this strategy, as Chamberlin explains, the scientist becomes “the parent of a family of hypotheses: and, by his parental relation to all, he is forbidden to fasten his affections unduly upon any one.”

What’s the secret to solving the puzzle? What set apart the successful participants from the unsuccessful ones? The unsuccessful participants believed they found the rule early on and proposed strings of numbers that confirmed their belief. If they thought the rule was “increasing intervals of two,” they generated strings like 8, 10, 12 or 20, 22, 24. As the experimenter validated each new string, the participants grew increasingly more confident in their initial brilliant hunch and assumed they were on the right track. They were too busy trying to find numbers that conformed to what they thought was the right rule, rather than discovering the rule itself. The successful participants took the exact opposite tack. Instead of trying to prove themselves right by generating strings that confirmed their hypothesis, they tried to falsify it. For example, if they thought the rule was “increasing intervals of two,” they would say, “3, 2, 1.” That string doesn’t follow the rule. They might then say, “2, 4, 10.” That string follows the experimenter’s rule, but doesn’t follow what most participants assumed was the right rule. The numbers game, as you may have guessed, is a microcosm for life. Our instinct in our personal and professional lives is to prove ourselves right. Every yes makes us feel good. Every yes makes us stick to what we think we know. Every yes gets us a gold star and a hit of dopamine. But every no brings us one step closer to the truth. Every no provides far more i...

“One mark of a great mind,” Walter Isaacson said, “is the willingness to change it.”48 When the world around you changes—when the tech bubble bursts or self-driving cars become the norm—the ability to change with the world confers an extraordinary advantage. “The successful executive is faster to recognize the bad decisions and adjust,” explains Walt Bettinger, the CEO of Charles Schwab, “whereas failing executives often dig in and try to convince people that they were right.”

Most of our decisions in life are based not on tests, but on hunches and limited information. We launch a new product, we change careers, or we try a new marketing approach—all without a single experiment. We blame a lack of resources for skipping the testing but don’t recognize the costs of new approaches that end up failing. Even when we conduct tests, we perform superficial dress rehearsals that double as exercises in self-deception. We conduct tests—not to prove ourselves wrong, but to confirm what we believe is true. We tweak the testing conditions or interpret ambiguous outcomes to confirm our preconceptions.

Rocket science offers a way forward with a deceptively simple principle: test as you fly, fly as you test. According to the principle, experiments on Earth must mimic, to the greatest extent possible, the same conditions in flight. Rocket scientists test the spacecraft as the spacecraft will fly. If the test is successful, the flight must take place under similar conditions. Any significant deviance between the test and the flight can cause catastrophe—whether it’s a rocket, a government website, your job interview, or your next product. In a proper test, the goal isn’t to discover everything that can go right. Rather, the goal is to discover everything that can go wrong and to find the breaking point.

You don’t need a fancy vacuum chamber or a large budget to find the breaking point of your own widgets. You can run tests on prototypes or preliminary versions of your products or services using a representative group of customers. All it takes is a willingness to design tests for the worst-case—rather than the best-case—scenario.

Testing as you fly requires a multilayered approach. Rocket scientists begin testing with the subcomponents—for example, the individual cameras that will form a rover’s vision system, as well as the cables and connectors. Once the cameras are fully assembled, the vision system is tested again as a whole. The reason for this approach is well summarized by a Sufi teaching: “You think that because you understand ‘one’ that you must therefore understand ‘two’ because one and one make two. But you forget that you must also understand ‘and.’”10 Components that otherwise function properly may refuse to play nice with each other after assembly. Put another way, systems may produce different effects than do the individual components standing alone.

In many ways, these simulations are tougher than the actual flight. They follow the old adage “The more you sweat in peace, the less you bleed in war.” When Neil Armstrong first began walking on the lunar surface, he noted how the actual experience was “perhaps easier than the simulations at one-sixth g,” referring to the reduced gravity on the Moon.28 Sweating the small stuff on Earth ensured that the same stuff didn’t make Armstrong bleed in space. Repeated exposure to problems inoculates astronauts and boosts their confidence in their ability to defuse just about any issue. When physics throws curveballs at them, their training kicks in. After Hadfield returned to Earth from a successful mission, he was asked if things had gone as planned. “The truth is that nothing went as we’d planned,” he responded, “but everything was within the scope of what we prepared

Boone’s secret is the same as any self-respecting astronaut: test as you fly. Train in the same environment you’ll experience on race day—while your competition trains from the comfort of a gym because it happens to be raining outside. “You don’t race on a treadmill with Netflix in front of you,” Boone says, “so you shouldn’t be doing your training like that.”

Put differently, these surveys failed the test-as-you-fly rule. Filling out a survey about reading a newspaper, and the actual act of reading a newspaper, are two different things. Gallup knew that for the test to work, it had to closely resemble the flight. So what did Gallup do to remedy the problem? He sent a team of interviewers into people’s homes to watch them read newspapers and mark each part of the paper as read or unread. Awkward? Yes. More accurate than surveys? Absolutely. “Almost without exception,” Gallup wrote, “later questioning proved… preliminary statements [in surveys] false.” Gallup’s analog experiment was the precursor to modern digital tracking. If you think his approach was creepy, remember that Netflix knows exactly what you watch, when you watch it, and whether you stopped the last season of House of Cards before it ended. Netflix knows, as Gallup did, that observation is far more accurate than self-reporting.

The mantra makes sense when you’ve got human lives at stake. But as a descriptor for how rocket science works, it’s misleading. There’s no such thing as a zero-risk rocket launch. You still have to compete with physics. You can plan for some mishaps, but the cosmic banana peel is always around the corner. Accidents are inevitable when you’re creating a controlled explosion in a machine as complex as a rocket. If failure weren’t an option, we never would have dipped our toes into the cosmic ocean. Doing anything groundbreaking requires taking risks, and taking risks means you’re going to fail—at least some of the time. “There’s a silly notion that failure’s not an option at NASA,” Elon Musk says. “Failure is an option here [at SpaceX]. If things are not failing, you are not innovating enough.”9 It’s only when we reach into the unknown and explore ever-greater heights—and in so doing, break things—that we move forward.

The goal isn’t to fail fast. It’s to learn fast. We should be celebrating the lessons from failure—not failure itself.

There are two responses to negative feedback from a credible source: Deny it or accept it. Every great scientist chooses the latter, and Squyres did the same. Each proposal he submitted to NASA was better than the one that came before it.