Stop listening to only the success stories
Survivorship Bias
As many of you know I am Nigerian by birth and it’s election season with a lot of theatrics and drama. Unsurprisingly, the new generation of journalists, who have refused to conform to the norms, have been uncovering a lot of misrepresentations and assumptions.
This brings me to the term called Survivorship Bias. Before I dig in, a quick Ad:
This week I caught up with the founder of Nigeria Agritech, ThriveAgric, while playing football. ThriveAgric provides access to finance, technology-based advice, extension services, and global and local markets to smallholder farmers in Africa.
Uka shared his story of building his business, and he managed uncertain times. Listen here Anchor, Apple, Google
What is Survivorship Bias?
Survivorship bias is a cognitive shortcut that makes you ignore everything that didn’t survive some kind of selection process, focusing instead on only the “winners” in a particular field.
History is famously written by the victors. And we’ve seen that over time people manipulate records and history because they triumphed even by hook or cook (or recount records based on their perspective which may not be reality). These people become the S.I unit of success. Everyone loves a survivor!
I believe survivorship bias is more of a statistical problem where the sample data is not completely random, but self-selected toward “survivor”. i.e. those who failed to survive are not represented in the sample, hence the statistics are biased towards survivors.
We rarely talk about the “losers.” Newspapers don’t report on what didn’t happen, your Crypto gurus on Twitter don’t talk about the times they lost all their money, and no one writes a deep analysis on all the charismatic founders that eventually failed after enjoying success.
We often over-attribute success to things done by the victor rather than to randomness, luck or other influences. This makes us draw out false lessons by exclusively studying victors without seeing all of the accompanying losers who acted in the same way but were not lucky enough to succeed.
Let me share some clear examples of survivorship bias:
Bill Gates dropped out of college and he went on to become a billionaire. So did Mark Zuckerberg and Elon Musk. We fail to account for the countless others who dropped out of college but are now sad and broke.
Asians are believed to be quite good at computer science and math. This isn’t entirely true, unfortunately. This is mostly a number game because Asia’s population is far greater than the whole of America’s population.
Everyone has that photogenic friend who would post perfect pictures on Instagram. However, these pictures are those that were carefully and naturally selected from 100s if not 1000s of other pictures, all by trial and error.
Most startups or founders are trying to replicate the path of successful startups. The problem is there are thousands of decisions, timing and luck that are involved which aren’t factored in. Remember Peloton whose success was spurred by Covid? Unfortunately, we don’t consider all the failed startups who may have come through the same path but failed.
I guess my point here is to stop looking for ready-made answers. Avoid crowd mentality and seek out your truth. Be selective and deliberate about the data sources you base your assumptions and decisions on. And finally, seeks out the stories of failures - there’s so much to learn.