Survivorship Bias
Why we solve the wrong problems with the wrong solutions.
If you work in the field of design thinking, I’m sure you’ve heard someone say, ‘But what’s the real problem to solve?’. In saying this, they’re pushing everyone to look beyond the symptoms of a problem or complaint and instead focus on the real opportunity or the root of the problem.
To use another well-known example, if you’d asked people in the late 1800’s how to improve their transportation, they would have said, get a faster horse. But what they really wanted was to get from one location to the next faster, easier, and more efficiently. Solving for the latter will generate real ideas and opportunities, for example the car. Solving for a faster horse is a dead end.
The moral? Choosing the right problem to solve is essential.
Some of you may have heard this story before, but it’s worth repeating. In World War II, many of the Allied Forces' planes returned to base beat up and with gaping holes where there should have been airplanes, as seen in the image above.
Trying to figure out how to make the planes more resistant to enemy fire was a matter of life or death. To try and aid in this effort, the Navy made a diagram of the locations where the planes were most badly damaged, based on the aircraft that made it back to base:
Many felt that the obvious solution was to better protect the areas that took the most gunfire.
One person has been credited with disagreeing. Abraham Wald pointed out that the airplanes that took this much gunfire had still made it back to base. They were the survivors. He argued that they didn’t need to solve the problems of the planes that made it back; they needed to solve for all the planes that hadn’t.
Instead of focusing on the areas that were hardest hit on the surviving planes, they should look at the areas that hadn’t been hit and figure out how to better protect them — knowing these must have been what took the other planes down.
This is called Survivorship Bias, its our tendency to focus on the survivors rather than the “non-survivors.” We hold up the winners as models instead of considering the weaknesses that caused others to fail.
“Survivorship bias is a form of selection bias that can lead to overly optimistic beliefs because multiple failures are overlooked, such as when companies that no longer exist are excluded from analyses of financial performance.” (Source)
For example, we’ll study why Amazon.com succeeds but not on why Pets.com failed.
Why it Matters
When approaching any problem to solve or area of opportunity, it’s important to be user-focused and look at the root of the problem. What are they really trying to accomplish? Towards what outcome? For what benefit? Target the root or the core instead of focusing on the obvious. And to include examples of what’s worked before and what hasn’t, considering both possibilities.
This example also highlights the importance of rigorous analysis of what appears to be a clear correlation of data to the next steps because it may not be so clear after all.
Thought Starters
- Are you solving the right problems?
- Are you considering multiple sides when looking at possible solutions?
- What haven’t you considered? Are you missing “non-survivors”?
Special Offer
If you missed our April summit or just want to revisit the presentations or key insights, you can check out both at https://www.northomegroup.com/independent-voices-creative-collisions/