WHAT IS SURVIVORSHIP BIAS
If you work a lot with data, this might be a familiar term. Survivorship bias is the phenomenon in which results (or survivors) of a process are treated disproportionately. Incomplete data sets, lack of context or incorrect interpretation of data is often the basis of this misconception. If you understand why survivorship bias occurs and you recognize the effect, it will help you better understand your data and make your analyzes more reliable and valid. In recent history we find numerous examples of this phenomenon, it has affected scientists, entrepreneurs, and researchers, among others.
WHAT DOES A FAIL HAVE TO TELL?
In the book “The Black Swan: The Impact of the Highly Improbable”, Nassin Taleb writes: “The cemetery of failed restaurants is very silent.” But focusing only on success and not looking into the fails will make you miss out on the full scope of your data and not really find understanding of how your processes actually function.
Success stories of entrepreneurs are often used as examples of how things should be done, but in addition to those few success stories, there are a multitude of entrepreneurs who don’t make it. Bill Gates (Microsoft), Jeff Bezos (Amazon) and Mark Zuckerberg (Facebook) are indeed successful in their businesses, but only have one side of the story to tell: how they made it and achieved their success. Many others who may have taken the exact same steps, have the exact same talent and also have shown 100% ambition have failed to make it – and their story is perhaps even more interesting. They can tell you what happened and what caused them to fail. These stories often contain wisdom from which we can deduce why things go wrong, why we fail. Focusing only on the “survivors” will stop you from getting the overall view and finding the flaws in your processes.
“The cemetery of failed restaurants is very silent.” – Nassin Taleb