Survivor bias（偏见,偏差）, occurs when you tend to assess successful outcomes and disregard failures. This sampling bias paints a more promising or even misleading picture of reality.

Survivor bias is a sneaky problem that tends to slip into analyses unnoticed. For starters, it feels natural to emphasize success, whether it's entrepreneurs, or survivors of a medical condition. We focus on and share these stories more than the failures.

Think about the famous college dropouts who became highly successful, such as Mark Zuckerberg, Steve Jobs, and Bill Gates. These successful examples might make you think a college degree isn't beneficial. However, that's survivor bias at work! These famous individuals are at the forefront of media reports. You hear more about them because they are extraordinary. You're not considering the millions of other college dropouts that aren't rich and famous. You need to assess their outcomes as well.

Survivor bias has even occurred in medical studies about severe diseases. Younger, healthier, and more fit patients tend to survive a disease's initial diagnosis more frequently. Hence, they are more likely to join medical studies. On the contrary, older, weaker patients are less likely to survive long enough to participate in studies. Consequently, these studies overestimate successful disease outcomes because they are less likely to include those who die shortly after diagnosis.

Undeniably, successful cases are usually more visible and easier to contact than unsuccessful cases. However, focusing on the high-performing successes and disregarding other cases introduces survivor bias. After all, you're leaving out a significant part of the picture as it's harder to collect data from the less successful members of a population. Incomplete data can affect your decision-making process. Put simply, survivor bias produces an inaccurate sample, causing you to jump to incorrect conclusions.

To minimize the impact of survivor bias, you should find ways to draw a representative sample from the population, not just a few of successful samples. That process might call for more expense and effort, but you'll get better results.