I couldn’t look away from the Titan documentary. As someone who’s spent years building teams and pushing technological boundaries, watching Stockton Rush’s (CEO OceanGate, died in his last submarine dive) journey felt uncomfortably familiar. The relentless drive, the impatience with bureaucracy, the absolute conviction that you’re revolutionizing an industry—I’ve been there. But somewhere in that familiar entrepreneurial passion, Rush crossed a line that cost five people their lives.
The Seductive Power of Visionary Thinking
Rush wasn’t a villain—he was a visionary who lost his way. His dream of democratizing deep-sea exploration was genuinely inspiring. For decades, only government-funded missions could reach the Titanic’s depth of 12,500 feet. Rush wanted to change that, making the impossible accessible to civilians willing to pay $250,000 for the experience.
The problem wasn’t his vision; it was how completely he became it.
The Data Tell the Story: According to maritime safety records, properly certified deep-sea vessels have a failure rate of less than 0.1%. Experimental, uncertified vessels? The numbers are staggeringly different—failure rates approach 15-20% in extreme depth applications. Rush knew these statistics but convinced himself his carbon fiber innovation would beat the odds.
This is what psychologists call “optimism bias”— (Read more) the tendency to overestimate positive outcomes while underestimating risks. Research by Nobel laureate Daniel Kahneman shows that entrepreneurs exhibit this bias at rates 3-4 times higher than the general population. It’s what makes us start companies against impossible odds, but it’s also what can make us ignore flashing red warning lights.
The Regulatory Rebellion: Understanding Both Sides
I get Rush’s frustration with certification bodies. I’ve watched brilliant innovations die slow deaths in regulatory purgatory. The FDA takes an average of 12 years to approve breakthrough medical devices. Aviation certification can stretch beyond a decade. When you’re burning through investor capital and your team is depending on you, these timelines feel like innovation killers.
Rush chose to operate in international waters specifically to avoid U.S. Coast Guard oversight. His argument? “Innovation doesn’t happen when you have to follow restrictive regulations.” On the surface, it’s compelling.
But here’s what the data actually show: Industries with robust safety regulations don’t just save lives—they often accelerate long-term innovation. The aviation industry, heavily regulated since the 1950s, has achieved remarkable safety improvements while continuously advancing technology. Commercial aviation fatality rates have dropped 95% since 1970, even as air travel increased 10-fold.
Companies like SpaceX prove you can work within regulatory frameworks while still pushing boundaries. SpaceX conducted over 50 successful launches before their first crewed mission, working closely with NASA throughout. Their approach: “Regulation as a design constraint, not a roadblock.”
When Expertise Becomes the Enemy
Perhaps the most troubling aspect of Rush’s story was his systematic dismissal of expert warnings. Multiple deep-sea engineers, including his own employees, raised concerns about the Titan’s carbon fiber hull design. Industry veterans warned that carbon fiber, unlike steel or titanium, doesn’t fail gradually—it fails catastrophically, with no warning.
Rush’s response? He fired critics and publicly stated that industry experts were “uninspiring” and stuck in old ways of thinking.
The Psychology Behind This: Research from Harvard Business School shows that as entrepreneurs become more successful, they increasingly discount external advice. It’s called “expert blindness”—the more passionate we become about our vision, the less we hear dissenting voices. In a study of 847 startup failures, 34% could be traced directly to founders ignoring critical technical feedback from domain experts.
This hits close to home for any leader who’s ever built something from scratch. You become so emotionally invested in your creation that criticism feels like personal attacks. But in safety-critical applications, this emotional attachment can literally be deadly.
The Success-Fame Convergence Trap
What struck me most about Rush’s interviews was how intertwined his personal identity had become with OceanGate’s success. He wasn’t just building a submersible company—he was becoming the visionary who would change deep-sea exploration forever. His LinkedIn described him as a “Innovator, explorer, and entrepreneur revolutionizing ocean access.”
The numbers paint a clear picture: According to venture capital data, founder-led companies where the CEO maintains more than 60% equity ownership have 40% higher failure rates in safety-critical industries. When personal wealth and reputation become completely tied to a single product’s success, rational risk assessment becomes nearly impossible.
Jeff Bezos spoke about this phenomenon at a leadership conference in 2019: “The moment you think you are your company, you stop making decisions in the company’s best interest. You start making decisions to protect your ego.”
Building Better Safety Cultures: What Actually Works
So how do we maintain entrepreneurial drive while protecting the people who trust us with their lives? I’ve seen effective approaches across multiple industries:
The Titan wasn’t just an engineering failure—it was a leadership failure that cost five lives. As entrepreneurs and executives, we must learn from Rush’s mistakes before we repeat them in our own domains. Because in the end, no innovation is worth destroying the trust people place in our judgment.
The next time you see a leader who is calm, steady, and quietly resilient, give them the ultimate compliment: call them a donkey.
Today, I visited a petting farm and learned about the characteristics of donkeys. Contrary to popular belief, which often associates the term “dumb” with donkeys, I discovered that they are not only hardworking but also possess exceptional intelligence and discipline. Donkeys are meticulous creatures who take pride in maintaining a clean and organized environment. They naturally assume the role of leaders within their families, providing protection and guidance to their loved ones. When engaged in their tasks, donkeys exhibit unwavering focus and dedication, demonstrating their remarkable capabilities. This made me curious about what I can learn from them.
While lions get the glory and eagles soar in leadership metaphors, the donkey, often mischaracterized as stubborn, embodies the most practical and effective leadership traits. This misunderstood animal demonstrates systematic thinking, unwavering focus, and methodical execution that modern CXOs and executives desperately need.
Donkeys never just charge forward. They pause, assess the terrain, calculate the load, and check if the path is safe. Once convinced, they move forward with calm assurance.
For leaders:
• Build decision frameworks instead of relying on gut calls.
• Use tools like pre-mortems and go/no-go gates before committing resources.
Once a donkey commits to a path, it doesn’t get distracted. No noise, no glamour—just step after step until the goal is reached.
For leaders:
• Set clear, measurable objectives each quarter.
• Use OKRs to keep teams aligned.
• Create “mission filters” so you only pursue what truly matters.
Donkeys don’t sprint. They move steadily, balancing energy and endurance. And because of that, they rarely burn out.
For leaders:
• Prioritize sustainable pace over short-lived bursts.
• Focus on compound growth, not just hockey-stick projections.
• Adopt Kanban or continuous delivery to encourage steady progress.
Donkeys carry impressive loads for their size—but they know their limits. If overloaded, they simply refuse to move. That’s not stubbornness; it’s wisdom.
For leaders:
• Protect your team from being overburdened.
• Define capacity clearly and say “no” when necessary.
• Delegate wisely instead of piling work on the same shoulders.
Leadership isn’t about charisma or brute force. It’s about focus, resilience, and knowing your limits. In other words, leading like a donkey.
Donkeys aren’t impulsive. They pause, assess the ground, and only move when it’s safe. They don’t burn themselves out sprinting, but they keep going, step after step—until the job is done. They carry heavy loads, but they also know when to stop and refuse more. That isn’t stubbornness; it’s wisdom about limits. And when they lead, it’s not through noise or force, but through presence, protection, and quiet guidance.
In a world where leaders often confuse speed for progress and noise for impact, we could use more donkey-like qualities: patience, focus, endurance, and balance.
So yes—when you meet a leader like that, don’t flatter them with the usual metaphors. Look them in the eye and give them the highest praise you can: “You lead like a donkey.”
The race to “solve all diseases” has begun, and AI is leading the charge.
Something remarkable is happening in pharmaceutical labs around the world. For the first time in history, drugs designed entirely by artificial intelligence are about to be tested in humans. Alphabet’s Isomorphic Labs will begin these groundbreaking trials in 2025, marking a potential turning point in how we discover new medicines.
Think about it: after decades of painstaking trial-and-error in drug discovery, we’re now watching AI systems design potential cures in a fraction of the time. Demis Hassabis, the Nobel Prize-winning CEO behind this breakthrough, believes we’re finally within reach of “solving all diseases.”
Here’s what traditional drug discovery looks like:
Imagine you’re searching for a needle in a haystack—except the haystack is the size of a football stadium, and you’re not even sure what the needle looks like. That’s essentially what pharmaceutical companies have been doing for decades.
The Traditional Timeline:
Now picture having a super-intelligent assistant that can scan the entire haystack in minutes, identify exactly what you’re looking for, and even suggest improvements. That’s what AI is doing for drug discovery.
Isomorphic Labs compressed this entire process to about four years—and they’re just getting started.
While Isomorphic Labs grabbed headlines, they’re not alone in this race. Other companies are achieving even more dramatic speed improvements:
The Speed Leaders:
What used to take nearly a decade now happens in 1-4 years. In some cases, it’s even faster. This isn’t just incremental improvement—it’s a complete transformation of how medicines are discovered.
The secret isn’t just faster computers—it’s smarter approaches. AI transforms drug discovery in three powerful ways:
1. Smart Target Hunting
Instead of researchers spending years in labs testing thousands of compounds, AI analyzes massive datasets to predict which molecules will work—often in just weeks.
2. Virtual Testing
Before expensive animal testing, AI can simulate how drugs will behave in the body. Think of it as a sophisticated video game that predicts real-world results.
3. Rapid Learning Cycles
Traditional research moves slowly: test, analyze, redesign, repeat. AI systems can run thousands of these cycles simultaneously, learning from each iteration.
This is how AlphaFold 3 and similar platforms are changing the game—they’re not just tools, they’re intelligent partners in the discovery process.
For Pharmaceutical Companies:
For Patients:
Before we get too excited, let’s address the elephant in the room. Despite these impressive timelines, some things simply can’t be rushed:
Regulatory Requirements Don’t Disappear
The FDA still requires extensive safety testing. Animal studies are mandatory. Long-term effects must be studied. No amount of AI can change these fundamental safety requirements.
Biology Is Still Complex
Cancer drugs, especially new ones, require extensive validation. The human body is incredibly complex, and AI is still learning to predict how drugs will actually behave in real patients.
Technology Is Still Maturing
These are first-generation AI platforms. They’re impressive, but they’re still evolving. Regulatory agencies are also still figuring out how to evaluate AI-designed drugs.
The good news? Even with these constraints, AI is already delivering meaningful speed improvements. As the technology matures, we can expect even better results.
For Technology Leaders:
The question isn’t whether to invest in AI drug discovery, it’s how fast you can move. This represents a fundamental platform shift, similar to how the internet transformed commerce. Early movers are building sustainable advantages.
For Business Strategy:
Watch the partnerships. Traditional pharma companies are racing to ally with AI-first companies. The successful combinations will likely dominate the next decade of drug discovery.
For Market Watchers:
Isomorphic Labs’ human trials in 2025 represent a crucial test case. Success could accelerate investment and regulatory acceptance. Failure might slow the entire field.
Isomorphic Labs’ upcoming human trials, mark more than just another drug study they represent a potential inflection point in medicine. While the company’s four-year timeline isn’t dramatically faster than optimized traditional development, it’s what comes next that matters.
As AI platforms mature and regulatory pathways streamline, we’ll likely see even more dramatic accelerations. The companies and countries that master this technology first will have enormous advantages in addressing humanity’s greatest health challenges.
The race to “solve all diseases” isn’t just a bold vision anymore—it’s becoming a measurable reality. And AI is leading the charge.
How the ancient art of paper folding teaches us Thomas Sterner‘s principles of discipline and focus
There’s something magical about watching a master origami artist transform a simple sheet of paper into an intricate crane, dragon, or flower. What starts as a flat, unremarkable square becomes something beautiful and complex through nothing more than strategic folds. This transformation embodies a profound truth about learning, growth, and achievement—one that Thomas M. Sterner captures brilliantly in his book “The Practicing Mind” through what I call the 4S Framework: Simplify, Small, Slow, and Short.
Sterner’s framework offers a counterintuitive approach to mastery in our fast-paced, instant-gratification world. Let’s explore each element through the lens of origami, then see how these principles revolutionize business thinking.
In origami, every complex creation begins with the same foundation: a single square of paper. No glue, no scissors, no elaborate tools—just paper and intention. The art lies not in adding complexity, but in finding the elegant simplicity within complexity.
Master origami artists don’t start by imagining the final crane; they focus on the next fold. Each fold is a simple action: valley fold, mountain fold, inside reverse fold. The complexity emerges from the accumulation of simple, deliberate actions.
The Business Connection: The most successful businesses often have the simplest core concepts. Amazon started with one idea: sell books online. Google began with one mission: organize the world’s information. Netflix simplified entertainment: movies by mail, then streaming. They didn’t launch with dozens of features—they perfected one simple value proposition first.
Every origami journey begins with a modest square of paper—often just 6 inches by 6 inches. You don’t need expensive materials or vast resources. The constraint of size actually enhances creativity and forces precision. Small paper means small mistakes, quick learning cycles, and lower stakes for experimentation.
When learning origami, you don’t start with a 1,000-step dragon. You begin with a simple boat or paper airplane. These small projects build fundamental skills while providing immediate satisfaction and confidence.
The Business Connection: The startup world has embraced this through the Minimum Viable Product (MVP) concept. Instead of spending years building the perfect product, successful entrepreneurs start small. Facebook began as a simple directory for Harvard students. Airbnb started with air mattresses in the founders’ apartment. Twitter emerged from a simple question: “What are you doing?”
Small beginnings allow for rapid iteration, reduced financial risk, and faster market feedback. They also make the seemingly impossible feel achievable.
Here’s where origami reveals its deepest wisdom: going slow actually makes you faster. When you rush through folds, you create imprecision that compounds throughout the model. A valley fold that’s slightly off becomes a major structural problem twenty steps later. You end up starting over, taking much longer than if you’d been deliberate from the beginning.
Experienced origami artists move with methodical precision. They study the diagram, understand the intended result, make the fold carefully, and ensure it’s correct before proceeding. This “slow” approach leads to flawless execution and faster overall completion.
The Business Connection: In business, “slow” means taking time to understand your market, validate assumptions, and build solid foundations. Companies that rush to scale often collapse under their own weight. Those that move deliberately—like Patagonia’s careful expansion or In-N-Out Burger’s methodical geographic growth—build sustainable, lasting enterprises.
Slow also means giving your team time to understand strategy, your customers time to adopt your product, and yourself time to develop genuine expertise. The paradox is that this patient approach ultimately accelerates long-term success.
Origami mastery doesn’t come from marathon folding sessions that leave you frustrated and fatigued. It comes from consistent, short practice periods. Fifteen minutes of focused folding is more valuable than two hours of distracted attempts.
Short sessions maintain engagement, prevent mental fatigue, and allow for better retention. Each brief practice builds on the previous one, creating steady progress without burnout. You might learn one new fold per session, but those folds compound into increasingly sophisticated models over time.
The Business Connection: The most effective business development happens in short, focused sprints rather than endless work marathons. The Pomodoro Technique, agile development cycles, and regular brief check-ins all reflect this principle.
Short also applies to goal setting. Instead of aiming to “transform the industry,” successful businesses set short-term, achievable milestones. Weekly objectives, monthly targets, and quarterly goals create momentum and maintain motivation while building toward larger visions.
The magic happens when these four principles work together. In origami, you simplify complex forms into basic folds, start with small projects and small pieces of paper, work slowly and deliberately, and practice in short, focused sessions. This approach doesn’t just create paper art—it develops patience, precision, spatial intelligence, and the ability to see complex systems as sequences of simple steps.
The same compound effect occurs in business. Companies that simplify their core offering, start small with their market, move slowly enough to build solid foundations, and focus on short-term achievable goals often outperform those that try to do everything at once.
For Entrepreneurs:
For Teams:
For Personal Development:
Perhaps the most profound lesson from origami is about the nature of creation itself. Every fold matters. Every decision has consequences that ripple through the entire structure. There are no shortcuts, but there is elegance in the process when you embrace the 4S principles.
Great leaders, like master origami artists, understand that transformation happens one fold at a time. They resist the urge to force outcomes and instead focus on perfecting the process. They know that rushing leads to structural weaknesses, while patience creates strength.
In our age of instant everything, origami offers a different path—one that mirrors Sterner’s insights about developing a practicing mind. The art teaches us that complexity emerges from simplicity, that small beginnings enable great achievements, that slow progress is often the fastest route to mastery, and that short, focused efforts compound into extraordinary results.
Whether you’re building a business, developing a skill, or pursuing any meaningful goal, the 4S framework provides a sustainable path forward. Like the origami artist who transforms a simple square into something beautiful, you can transform your aspirations into reality—one deliberate fold at a time.
The next time you feel overwhelmed by the complexity of your goals, remember the origami master. Pick up that simple square of paper. Make one fold. Then another. Trust the process, embrace the principles, and watch as something extraordinary emerges from the most humble beginnings.
What will you create with your next fold?
Have you ever wondered why some people seem to effortlessly turn their dreams into reality while others remain perpetually stuck in the planning phase? Bernard Roth’s “The Achievement Habit: Stop Wishing, Start Doing, and Take Command of Your Life” offers a refreshingly honest answer: achievement isn’t about having the best ideas or the most talent, it’s about developing the right habits and taking consistent action.
Roth, a Stanford professor and co-founder of the renowned d.school, brings decades of design thinking expertise to personal development. His central thesis is revolutionary in its simplicity: achievement is a habit that can be learned, practiced, and strengthened like a muscle. Drawing from real student transformations in his Stanford class “The Designer in Society,” Roth demonstrates that the same design thinking principles used to solve complex organizational problems can redesign your entire life.
The book’s power lies in its practical approach. Rather than offering feel-good platitudes, Roth presents a systematic method for breaking through self-imposed limitations and creating lasting change.
One of the book’s most profound insights is that meaning is entirely subjective—we assign significance to everything in our lives, and these assignments shape our actions and outcomes. Roth argues that changing how you label and view situations can unlock creativity and positive transformation.
This isn’t just positive thinking; it’s strategic reframing. When you recognize that your interpretation of events—not the events themselves—determines your response, you gain tremendous power to change your experience. The practical exercise here is simple but transformative: regularly question your assumptions and consciously relabel familiar situations to open new possibilities.
Perhaps the book’s most controversial chapter tackles our relationship with excuses. Roth boldly states that most reasons we give for our actions are simply sophisticated excuses designed to protect our self-image. While this might sound harsh, it’s liberating once you embrace it.
The author isn’t advocating for social rudeness, externally, reasons may still be necessary. But internally, questioning every reason forces honest self-assessment. If something truly matters to you, your actions should reflect that priority without elaborate justification. This shift from explanation to action is where real change begins.
The distinction between “trying” and “doing” runs throughout the book like a golden thread. Roth emphasizes that real achievement comes only through committed action, not good intentions or endless discussions. There’s a fundamental difference between someone who says “I’ll try to exercise” and someone who simply exercises.
This connects to his advocacy for prototyping, taking small, experimental steps to build momentum. Rather than waiting for the perfect plan, start with imperfect action. Small wins build confidence and break the inertia that keeps most people stuck in perpetual preparation mode.
In our age of endless information and analysis paralysis, “The Achievement Habit” offers a refreshing antidote. Roth’s background in design thinking brings practical structure to personal development, moving beyond motivation to methodology. The book doesn’t just inspire—it instructs.
What makes this particularly relevant is how Roth addresses modern challenges like overthinking, perfectionism, and the tendency to substitute planning for action. His emphasis on collaboration and asking for help counters our increasingly isolated approach to personal growth.
“The Achievement Habit” succeeds because it treats personal development as a design problem rather than a motivation issue. Roth shows that achievement isn’t about having the right personality or waiting for inspiration, it’s about building systems and habits that consistently move you forward.
The book’s real strength lies in its integration of mindset shifts with practical action. It’s not enough to change how you think; you must change what you do. And it’s not enough to take random action; you must align that action with an empowering self-image and clear purpose.
If you’re tired of books that make you feel good but don’t create lasting change, “The Achievement Habit” offers something different: a proven framework for turning intentions into results. Roth’s message is both challenging and hopeful, you have more control over your outcomes than you think, but only if you’re willing to stop making excuses and start taking consistent action.
The question isn’t whether you can achieve more in your life. The question is whether you’re ready to make achievement a habit.
When we conduct surveys or studies or ask for feedback, we often focus on the responses we receive—analyzing patterns, drawing conclusions, and making decisions based on this data. However, what about the voices we never hear? The participants who decline to respond, hang up the phone, ignore the email, or simply cannot be reached? Their absence from our data can tell an important story of its own—one that might significantly alter our conclusions if we knew it.
This is the challenge of non-response bias, a systematic error that occurs when those who respond to a survey differ in meaningful ways from those who don’t respond. Unlike sampling error, which can be addressed through larger sample sizes, non-response bias can persist or even worsen as you collect more data if the underlying pattern of non-response remains consistent.
Non-response bias occurs when people who don’t respond to surveys or studies have characteristics that differ from those who do respond, leading to skewed results that don’t accurately represent the target population. In statistical terms, it’s a type of selection bias where the selection process is driven by the subjects themselves rather than the researchers.
For example, imagine a university sending out a satisfaction survey to all its graduates. Those who had particularly positive or negative experiences might be more motivated to respond than those with moderate experiences. If the survey concludes that 40% of graduates were extremely satisfied and 30% extremely dissatisfied, this might represent a distorted picture compared to the true distribution.
Perhaps the most famous historical example of non-response bias occurred during the 1936 U.S. presidential election. The Literary Digest, a respected magazine, conducted what was then the largest political poll in history, mailing out surveys to over 10 million Americans. Based on the 2.4 million responses they received, they confidently predicted that Republican Alf Landon would defeat incumbent Democrat Franklin D. Roosevelt in a landslide.
Instead, Roosevelt won in one of the most lopsided victories in American electoral history, carrying 46 of 48 states.
What went wrong? The Literary Digest had compiled their mailing list from telephone directories, club memberships, and magazine subscriptions—all indicators of higher socioeconomic status during the Great Depression. Additionally, those who responded were more likely to be politically engaged and opposed to Roosevelt’s New Deal policies. The combined effect of this sampling bias and non-response bias led to a spectacular polling failure that effectively ended the magazine’s reputation.
Health surveys frequently suffer from non-response bias. People with serious health conditions may be too ill to participate in surveys, while those who are health-conscious might be overrepresented in responses. This can lead to underestimating disease prevalence and overestimating healthy behaviors in the general population.
A striking example comes from the Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System (BRFSS), which has seen declining response rates over time. Research comparing early BRFSS data to subsequent health records found that respondents were generally healthier than non-respondents, leading to potentially optimistic assessments of population health.
Corporate employee satisfaction surveys often suffer from non-response bias. Employees who feel extremely negative about their workplace may fear retaliation despite promises of anonymity. Conversely, highly satisfied employees might not feel motivated to respond because they see no problems needing attention.
Additionally, the busiest and most overworked employees—whose feedback might be particularly valuable regarding workload issues—often don’t have time to complete voluntary surveys, creating a systematic gap in the data.
The dramatic bimodal distribution of online product reviews (many 5-star and 1-star reviews, fewer in the middle) is a classic example of non-response bias in everyday life. Customers with strong positive or negative experiences feel motivated to leave reviews, while those with average experiences typically don’t bother. This creates a “J-shaped” or “U-shaped” distribution that may not reflect the true customer experience.
Several factors contribute to non-response bias:
Some potential respondents simply cannot be reached or face barriers to participation:
The subject matter itself can influence who responds:
As people are increasingly bombarded with requests for feedback:
In an era of data breaches and privacy concerns:
How can researchers determine if non-response bias is affecting their results? Several approaches can help:
If demographic information about the target population is available from reliable sources (like census data), researchers can compare the demographic profile of respondents to that of the overall population. Significant differences may suggest non-response bias.
Research suggests that late responders often share characteristics with non-responders. By comparing those who responded immediately to those who only responded after multiple reminders, researchers can estimate the direction and magnitude of non-response bias.
The gold standard approach is to conduct intensive follow-up with a sample of non-respondents, using additional incentives or different contact methods to secure their participation. The responses from this group can then be compared to the original respondents to identify systematic differences.
By analyzing how survey results change as additional waves of responses come in (after reminders or follow-ups), researchers can extrapolate what the results might look like if everyone had responded.
While it’s impossible to eliminate non-response bias entirely, several strategies can help mitigate its effects:
While reducing non-response bias is important for research validity, there’s an ethical balance to strike. Persistent follow-up can cross the line into harassment, and excessive incentives may become coercive. Researchers must consider:
The COVID-19 pandemic created unique challenges for researchers studying the disease’s spread and impact. Early studies relied heavily on voluntary participation, potentially missing:
Some research teams addressed these issues by:
These efforts revealed important disparities in COVID-19’s impact that might have been missed with conventional approaches.
For those who use data rather than collect it, awareness of non-response bias is equally important:
When presented with survey results, ask:
Quality research will acknowledge limitations and potential biases. Be skeptical of results that claim perfect representativeness with low response rates.
No single data source is perfect. Triangulate information from different sources with different methodological strengths and weaknesses.
If survey results seem dramatically different from expectations or other data sources, non-response bias may be a factor worth considering.
Non-response bias represents one of the most persistent challenges in survey research, and its importance has grown as response rates have declined across countries and methods. Rather than seeing it as merely a methodological nuisance, we should view addressing non-response bias as an opportunity to hear diverse voices and understand the full spectrum of human experiences.
By acknowledging who might be missing from our data, implementing strategies to include them, and remaining humble about the limitations of our methods, we can work toward research that more accurately represents the populations we study.
The story told by silence—by those who don’t respond—can be as important as the story told by those who do. In the pursuit of truth and understanding, we must listen carefully to both.
Looking beyond what survived to understand the complete picture
When we study history, we naturally focus on what remains: the buildings still standing, the books preserved through centuries, the businesses that thrived, the medical treatments that worked. This tendency creates what statisticians call “survival bias” – a logical error where we concentrate on people or things that made it past some selection process while overlooking those that did not, leading to false conclusions and distorted perspectives.
While the bullet-hole-riddled WWII aircraft example is perhaps the most famous illustration of survival bias, history offers us countless other illuminating cases that reveal how this cognitive error shapes our understanding of the past and influences our decisions today.
During the Industrial Revolution and early 20th century, medical researchers made a puzzling discovery: factory workers, despite laboring in what we now know were often hazardous conditions, frequently appeared healthier in statistical studies than the general population.
This counterintuitive finding, known as “the healthy worker effect,” represented a classic case of survival bias. Only individuals with robust constitutions could endure the punishing physical demands of factory work. Those who became ill simply disappeared from the workforce—and consequently from the studies—creating a false impression about working conditions.
The healthiest workers remained visible in the data, while those whose health deteriorated became invisible. This statistical illusion delayed necessary workplace safety reforms and obscured the true human cost of industrialization for decades. Only when researchers began tracking workers longitudinally and accounting for those who left the workforce did the actual health impacts become apparent.
We marvel at structures like the Roman Pantheon, with its magnificent unreinforced concrete dome that has stood for nearly two millennia, while modern concrete often deteriorates within decades. This observation has led many to conclude that ancient Roman engineers possessed superior construction knowledge that was somehow “lost” to history.
However, this represents a classic survival bias. What we see today are only the most exceptional examples of Roman architecture—the statistical outliers that survived earthquakes, wars, and the relentless erosion of time. For every Pantheon or Colosseum that remains, thousands of ordinary Roman structures collapsed long ago and were forgotten.
Recent archaeological work has revealed that Roman concrete wasn’t universally superior—many structures failed quickly, but these failures don’t remain for us to observe. The structures that survived often did so because they were built in geologically stable areas, constructed with extraordinary resources by the empire’s finest engineers, or continuously maintained and restored throughout history.
When we consider only the survivors, we mischaracterize the typical Roman building experience and create false narratives about “lost knowledge,” when in fact modern materials science has produced far more reliable and consistently durable building materials.
Our understanding of medieval thought and culture is profoundly shaped by survival bias. The vast majority of surviving manuscripts from the Middle Ages come from monasteries and religious institutions—texts deemed worthy of careful preservation and painstaking reproduction by scribes.
This creates a fundamentally skewed historical record. Religious perspectives, classical works approved by the Church, and writings by social elites are dramatically overrepresented, while secular literature, folk traditions, dissenting religious views, and the perspectives of ordinary people were far less likely to be preserved.
Historians estimate that less than 1% of all medieval manuscripts survived to the modern era. This tiny fraction profoundly shapes our perception of medieval society, making it appear more uniformly religious and intellectually constrained than it likely was. Recent archaeological finds, like the Novgorod birch bark documents in Russia—everyday letters written by ordinary citizens—suggest a much more diverse intellectual landscape than surviving formal manuscripts indicate.
The deadly influenza pandemic of 1918-1919 became known as the “Spanish Flu” not because it originated in Spain or because Spain suffered more severely, but because of a quirk of information survival. As a neutral country during World War I, Spain had no wartime press censorship, unlike most other affected nations.
While countries like the United States, Britain, France, and Germany suppressed news about the outbreak to maintain wartime morale, Spanish newspapers reported freely on the disease, including the illness of their king, Alfonso XIII. This created the false impression that Spain was uniquely affected when the pandemic was truly global in scope.
Modern research suggests the virus likely originated in the United States or China, but the survival bias in public information—with Spanish reports “surviving” censorship while others didn’t—created a historical distortion that persists in the pandemic’s name over a century later.
When we study literature from past centuries, we focus on what literary scholar Franco Moretti calls “the canonical 1%”—the tiny fraction of published works that have been preserved, anthologized, and continuously read. This creates the illusion that past eras produced mostly masterpieces, unlike our own time with its mix of great, good, and forgettable works.
In reality, Sturgeon’s Law—the principle that “90% of everything is crud”—applied just as much to Victorian novels or Renaissance plays as to modern literature. For every Shakespeare, there were dozens of forgotten playwrights; for every Jane Austen, hundreds of forgotten novelists whose works didn’t survive the ruthless filter of time.
This survival bias distorts our perception of literary history and creates unrealistic standards for contemporary writers. It also means our understanding of past literary cultures is based almost entirely on exceptional outliers rather than typical works.
Medical history provides particularly consequential examples of survival bias. Before the advent of rigorous clinical trials, doctors primarily recorded and passed down treatments that seemed to work, creating a body of medical literature rife with survival bias.
When patients recovered after a particular treatment, the treatment received credit—regardless of whether recovery might have happened anyway. Treatments that failed were less likely to be documented or, if documented, less likely to be repeatedly cited in medical texts.
This created a medical canon filled with ineffective or even harmful treatments that persisted for centuries. Bloodletting, for instance, remained a standard medical practice for over 2,000 years despite causing more harm than good in most cases. It survived because doctors noticed and remembered the subset of patients who improved after bloodletting (often despite the treatment, not because of it), while minimizing or forgetting the many who deteriorated.
Only with the development of controlled trials in the 20th century, explicitly designed to counter survival bias by tracking all outcomes, did medicine begin to systematically separate truly effective treatments from those that merely appeared effective due to selective observation.
Management literature is notorious for survival bias. Books analyzing “great companies” often study only businesses that succeeded, drawing conclusions about their practices without examining whether failed companies followed the same practices.
A famous example comes from Jim Collins’ business bestseller “Good to Great,” which analyzed companies that transformed from average to exceptional performers. Several companies praised in the book, including Circuit City and Fannie Mae, subsequently collapsed or required government bailouts, raising questions about the methodology’s validity.
By studying only “survivors,” such analyses often mistake luck for skill and correlation for causation. They identify practices that might be common among successful companies but fail to note these same practices may be equally common among failed ones.
When Napoleon invaded Russia in 1812, he began with approximately 450,000 soldiers. Only about 10,000 returned. Historical accounts of the campaign often focus disproportionately on these survivors’ experiences, creating a narrative heavily weighted toward the experiences of those who endured the entire ordeal.
The famous winter retreat from Moscow features prominently in these accounts, with harrowing descriptions of extreme cold and starvation. While these conditions were certainly devastating, survival bias obscures the fact that more of Napoleon’s troops died during the summer advance than during the winter retreat. Disease, heat exhaustion, and Russian guerrilla tactics decimated the Grande Armée before winter arrived.
By focusing primarily on winter survivors’ accounts, historical narratives overemphasized cold as the decisive factor while underrepresenting the many who perished from other causes earlier in the campaign.
These examples reveal how survival bias fundamentally shapes our understanding of history. To counter this bias, historians increasingly employ methodologies that actively search for what hasn’t survived, using archaeological evidence, statistical modeling, and cross-cultural comparisons to fill in historical blind spots.
As consumers of history, we should approach historical narratives with healthy skepticism, always asking: What might be missing from this picture? Whose voices weren’t preserved? What failures disappeared from the record?
Acknowledging survival bias doesn’t just give us a more accurate view of history—it offers practical wisdom. When we recognize that failure is underrepresented in our understanding of the past, we gain valuable perspective on our own setbacks and the statistical nature of success.
The real lesson of survival bias is that failure is both common and instructive. By seeking out and studying failures rather than focusing exclusively on survivors, we gain insights that would otherwise remain hidden. In business, science, medicine, and personal development, understanding what doesn’t work can be just as valuable as knowing what does.
History’s greatest progress often comes not from replicating past successes, but from analyzing past failures—the very data points that survival bias tends to erase. By actively countering this bias, we develop a richer, more accurate understanding of both history and the present.
As the philosopher George Santayana famously observed, “Those who cannot remember the past are condemned to repeat it.” To that, we might add: “Those who remember only the surviving parts of the past are condemned to misunderstand it.”
You know that moment in a business review where someone says, “We’ll definitely hit the target. It’s only September.” That’s optimism bias. It’s not just a mindset—it’s a recurring guest star in strategy decks, project timelines, and sales forecasts.
Optimism bias is the human tendency to believe that we’re less likely to encounter negative outcomes and more likely to succeed, even when evidence suggests otherwise. It’s why launch dates look like fairy tales and why budgets are often as tight as that last seat on a budget airline.
In business, it shows up with a suit and a smile:
“This will only take two weeks.” (Famous last words.) “The client will definitely sign this order.” (Spoiler: They won’t.) “We can absorb this scope change without affecting delivery.” (Said no Gantt chart ever.)
Project Timelines: Always on time, until they’re not. Gantt charts get high on hope. Sales Forecasts: Every lead is “hot.” But apparently, half are in Antarctica. Product Launches: MVPs become FOMOs (Fear Of Missing Out), loaded with “just one more feature.” Change Management: “People will adapt quickly.” Right after they stop resisting it entirely.
We’re wired for progress and positivity. In fact, leaders often need to be optimistic to inspire teams and investors. But unchecked optimism can become a strategic liability, leading to budget overruns, missed milestones, and serious trust erosion.
Despite these cautions, some optimism remains valuable. As research psychologist Tali Sharot notes, “Optimism pushes us to take risks and attempt difficult things.” The goal isn’t eliminating optimism, but tempering it with reality.
The next time you’re planning an office move, renovation, or technology implementation, ask:
1. What’s our historical accuracy on similar projects?
2. What specific complications might we face that aren’t in our current plan?
3. What would more experienced outsiders estimate for this project?
4. Have we built meaningful contingencies for time, budget, and resources?
By acknowledging optimism bias, we can harness its motivational benefits while avoiding its planning pitfalls. The result? Office changes that actually meet expectations—perhaps the most optimistic outcome of all.
Here’s how to stay hopeful without losing your head (or your quarterly bonus):
Run Pre-Mortems: Before the kickoff, imagine it all went sideways. What caused it? Fix those now. Use RYB Indicators: Red-Yellow-Green status makes optimism earn its stripes. Build Buffers (Secretly): Be the realist who adds padding to timelines—but doesn’t advertise it. Listen to the Skeptics: That person always raising risks? Give them a doughnut. Then listen. Measure Backlog, Not Just Velocity: “Hope is not a strategy.” Data is.
In Summary: Optimism Is a Leadership Asset, When Balanced
Optimism bias isn’t the enemy. It’s your over-caffeinated cousin, fun to have around, but don’t let it drive. Combine its energy with critical thinking, and you’ve got a solid business partner.
If your project plan reads like a wish list to Santa, it’s time for a reality check. Stay positive—but don’t forget to pack an umbrella.
Have you ever stubbornly held onto your initial judgment despite mounting evidence to the contrary? That’s conservatism bias at work—our tendency to insufficiently update our beliefs when presented with new information.
We pride ourselves on being rational thinkers, weighing evidence objectively before forming conclusions. Yet cognitive science reveals a systematic flaw in how we process new information: conservatism bias. This tendency to insufficiently revise our beliefs when presented with new evidence affects everything from personal finances to organizational strategy.
Conservatism bias occurs when people update their existing beliefs too slowly in the face of new, relevant information. First documented by psychologist Ward Edwards in the 1960s, this bias shows how we tend to “anchor” to our initial judgments, making only modest adjustments even when confronted with substantial contradictory evidence.
Unlike confirmation bias (where we seek information supporting our existing views), conservatism bias focuses on how we process new information once we encounter it—typically giving it less weight than statistical reasoning would suggest is appropriate.
Consider an investor who believes a particular stock is undervalued. When the company releases disappointing quarterly earnings, they might acknowledge this negative news but still underestimate its significance. Research from the Indian Securities and Exchange Board shows retail investors typically adjust their price expectations by only 40% of what would be statistically justified following earnings surprises, whether positive or negative.
A 2020 study in the Indian Journal of Medical Research found that physicians who made initial diagnoses were 30% less likely to completely revise their assessment when contradictory test results arrived compared to doctors seeing the case fresh. This “diagnostic momentum” demonstrates how early judgments resist appropriate updating.
Organizations frequently underreact to market changes that challenge their existing business models. Kodak famously recognized the threat of digital photography (their engineers actually invented the first digital camera in 1975) but significantly underweighted this evidence when planning their future, clinging to their film-based business model until it was too late.
Several factors contribute to conservatism bias:
Thoroughly revising beliefs requires significant mental energy. It’s simply easier to make minor adjustments to existing views than to completely reconsider our position.
We tend to overestimate the accuracy of our initial judgments. This overconfidence makes us less receptive to evidence suggesting we might be wrong.
Humans have a natural tendency to prefer existing states over change. This status quo bias reinforces conservatism in updating beliefs.
Changing our minds dramatically can feel uncomfortable, especially when we’ve publicly committed to a position. This social pressure reinforces incremental rather than transformative belief updates.
Using numerical probabilities rather than vague beliefs makes it easier to update appropriately. For instance, assigning specific likelihood percentages to potential outcomes forces more rigorous updating when new evidence arrives.
People without attachment to initial judgments can more objectively assess new information. Creating “red teams” tasked with challenging existing views helps organizations overcome institutional conservatism bias.
Decide in advance what evidence would change your mind, before seeing the results. This prevents moving the goalposts when confronted with belief-challenging information.
Named after 18th-century mathematician Thomas Bayes, Bayesian reasoning provides a formal framework for updating probabilities based on new evidence. Even informal Bayesian thinking—explicitly considering both prior beliefs and the strength of new evidence—can improve belief updating.
Conservatism bias isn’t just an academic curiosity, it has substantial real-world consequences. Companies that fail to adequately update their strategic thinking face extinction. Investors who insufficiently revise their market views sacrifice returns. Medical professionals who inadequately integrate new test results may miss critical diagnoses.
By recognizing our tendency toward conservatism bias, we can deliberately counteract it, ensuring that our beliefs more accurately reflect all available evidence rather than giving undue weight to our initial judgments.
The next time you encounter information challenging what you believe, ask yourself: Am I giving this evidence the weight it truly deserves, or am I being conservative in updating my beliefs?
I recall when I brought my Jeep, with very peculiar and unique grey color, I suddenly I’m seeing grey Jeep everywhere. On my commute, in parking lots, at the grocery store—they’re multiplying like rabbits! Or are they? This phenomenon has a name: selective attention bias.
Let me share what I’ve learned about this fascinating quirk of our minds and how it shapes our daily experiences, both personally and professionally.
Selective attention bias occurs when our minds prioritize information that aligns with our current focus or interests while filtering out everything else. As cognitive psychologist Daniel Kahneman explains in his book “Thinking, Fast and Slow,” our brains have limited processing capacity and must be selective about what information receives our conscious attention.
“We can be blind to the obvious, and we are also blind to our blindness,” Kahneman writes. This blindness isn’t a flaw, it’s a feature that helps us navigate an overwhelmingly complex world.
That experience with my Jeep Compass? It has another name: the Baader-Meinhof Phenomenon or frequency illusion. Once something enters your awareness, you start noticing it everywhere.
Stanford linguistics professor Arnold Zwicky coined the term “frequency illusion” in 2006 to describe this cognitive bias. The thing isn’t actually more common, you’re just more attuned to it 😀.
Look at the FedEx logo. Do you see the arrow between the “E” and “x”? Once someone points it out, you can’t unsee it. But many people go years without noticing this clever design element.
The Amazon logo has an arrow that points from A to Z (suggesting they sell everything) while forming a smile. Before someone mentions it, most people only see the smile without noticing the A-to-Z connection.
The Toblerone logo contains the silhouette of a bear hidden in the mountain imagery, a nod to Bern, Switzerland (known as the “City of Bears”) where the chocolate was created. Once seen, it’s obvious, but many chocolate lovers miss it completely.
We tend to notice information that confirms our existing beliefs while overlooking contradictory evidence. This confirmation bias affects everything from which news sources we trust to which products we buy.
Marketers leverage selective attention brilliantly. As marketing professor Jonah Berger notes in his book “Contagious,” “People don’t think in terms of information. They think in terms of narratives.” Brands create narratives that align with your current focus, making their products seemingly appear everywhere.
Being aware of selective attention bias can help us grow. By consciously exposing ourselves to diverse perspectives, we can counteract our brain’s natural tendency to filter information that challenges our worldview.
Last month, I was researching ergonomic office chairs for myself (exciting, I know). Within days, I started noticing office chair ads everywhere online, colleagues’ chairs during video calls, and even found myself analyzing seating in coffee shops.
Was the universe suddenly obsessed with office furniture? Nope—just my brain selectively focusing on what had recently become important to me.
Understanding selective attention bias has made me a better professional:
As American psychologist William James observed back in 1890, “My experience is what I agree to attend to.” By becoming conscious of our selective attention, we gain more control over our experience of the world.
What are you selectively attending to today? Look around, you might be surprised by what you’ve been missing!