Survivorship Bias is the logical error of concentrating on the people or things that survived some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways.

It refers to our propensity to analyze the survivors of a situation without considering the failures that never made it into the data set.

For example, highly successful entrepreneurs are often the subject of articles and books that attempt to determine the critical factors that led to their wealth and fame. Was it their drive and ambition, their ability to work on 4 hours sleep or their creativity that led to their success? For each of these business superstars, there were thousands of run of the mill entrepreneurs who achieved either modest success or complete failure. Without studying them as well, how would we know that they didn’t share some of the characteristics of the highly successful folks? How could we tease out the role of luck and circumstance from genuine success factors?

Survivorship Bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance.

Summing-up: Survivorship Bias is our tendency to include only successes in statistical analysis, skewing or even invalidating the results.