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The Hidden Costs of Survivorship Bias in the Tech Industry

Survivorship bias is a phenomenon in which our understanding of an event or process is skewed by focusing only on the survivors.

This phenomenon has been successfully used in various historical contexts, from war efforts to pet care. But what are the implications of utilizing survivorship bias in the tech industry today?

Abraham Wald was a statistician who escaped Nazi persecution during World War II and became one of the most significant contributors to the American war efforts. With only observation at his disposal, he crafted a heat map that revealed patterns even seasoned veterans had failed to detect – ultimately ensuring the survivability of bomber crews.

On the other side of this spectrum, Drs. Whitney and Mehlhaff’s research on cats falling from NYC window heights confirmed that cats genuinely have nine lives; amazingly, cats falling from over seven stories resulted in only one fatality, and those from nine stories or higher resulted in no more than one fracture!

Eric Yuan further exemplifies this phenomenon with his unconventional approach to problem-solving that enabled Zoom’s incredible success during the pandemic.

By proactively understanding how their system could collapse under unprecedented demand, Zoom could skyrocket without having its servers overworked or crashed. All three provide examples where survivorship bias appears triumphant; however, it begs us as an audience then: what are we missing when we’re captivated by these stories?

Historical Context

Abraham Wald’s contributions during WWII exemplify how understanding survivor bias can yield powerful solutions that revolutionize military operations worldwide. By considering those who had likely died but whose experiences weren’t being recorded (since they could not report them), he created an innovative plan that eventually turned the tide of battle against Axis forces dramatically shifted due largely to this methodical yet creative solution.

Instead of merely looking for “winners” within data sets about combat deaths, Abraham Wald used his foresight and ingenuity to address unseen information skewers understanding knowledge related to casualties, effective tactics increase survival rates.

The successes found within Whitney and Mehlhaff’s study also reflect survivor bias. At the same time, there were few fatalities resulting from cat falls regardless of story height.

Evidence suggests a greater chance of life extremely high drops. This remarkable tale adds another dimension of context associated with selection effects experienced beyond humans and animals alike, convincing proof ‘nine lives” legend is true enough to leave room to wonder if there are other tales untold because they didn’t survive to tell.

Eric Yuan illustrates yet again the concept at play. His proactive approach is unparalleled—by anticipating system failures and scaling capabilities for future growth, zoom rapidly achieved full server utilization during the Covid-19 pandemic. Consequently, much attention was given success spotlight, overshadowing some crucial questions that should be asked.

How does leadership implement such plans to prevent crash situations arise the first place? Is failure becoming celebrated tool innovation? Do companies stand to lose out long run relying solely on survivors’ skillset impart?

Implications & Consequences for Tech Industry

We can often get the wrong idea about success stories in the tech world by focusing too much on the few people who have made it. We may also forget to look at what made them special and successful.

We need to be careful about relying only on information from successful companies. If we only look at companies that have already achieved success, we may think that it’s easy to do well in business, which isn’t always true. Ignoring the experiences of unsuccessful companies can distort our understanding of what it takes to be successful.

Sometimes, leaders can make good decisions that try to protect people, but it’s not always appreciated until something bad happens and people have to be rescued because of it. Then it’s too late.

Companies have to be careful when using data sets, as they can have a lot of risks. To make sure tech businesses do well, entrepreneurs must use a comprehensive way to get a clear view of the company’s operations, so that they can avoid any potential problems.


Survivorship bias can stop us from understanding why some tech businesses succeed while others fail. If we only listen to the stories of winners, we won’t get an accurate picture of how they did it. To really understand how different tech companies got to where they are, we need to look at the context and the outcomes of both successes and failures.

We need to come up with different strategies that protect us from the risks associated with using survivor information to solve tough problems. We have to make sure that everyone involved knows all the possible solutions so that we can pick the best one and avoid more bad situations in the future.

The author partially generated this content with GPT-4 & ChatGPT, OpenAI’s large-scale language-generation models. Upon developing the draft, the author reviewed, edited, and revised the content to their liking and took ultimate responsibility for the content of this publication.





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