Data bias (volume II)
I want to share more examples of data bias and human perception of data. If you are not familiar with the topic, my previous blog, “Probability or Plausibility?”, “What does data indicate?” should pave a foundation for you to understand this article more efficiently.
There is nothing wrong with the data, but how we collect and understand it leads to incorrect answers. We collect data selectively to support the opinion we agree with and against the one we disagree with. This is a typical demonstration of confirmation bias. We allow our cognitive bias to do so primarily because the human mind is designed this way and our tendency to jump to conclusions without further consideration.
Example of reciprocity styles
In his book “Give and Take”, Adam Grant categorized three types of reciprocity styles: giver, taker and matcher. Unfortunately, research demonstrates that givers sink to the bottom of the success ladder. Across a wide range of meaningful occupations, givers are at a disadvantage: they make others better off but sacrifice their success. There is even evidence that compared with takers, on average, givers earn 14 per cent less money, have twice the risk of becoming victims of crimes, and are judged as 22 per cent less powerful and dominant. Based on this data, you might conclude that you should adopt a taker or matcher reciprocity style to succeed.
Nevertheless, guess who is at the top of the ladder – takers or matchers?
Neither. It is the givers again. Givers dominate the bottom and the top of the success ladder. Across occupations, if you examine the link between reciprocity styles and success, the givers are likelier to become champs, not only chumps. In most cases in our lives, we have been experiencing a small part of the whole picture, and we believe this is what the entire picture looks like.
The story of the blind men and an elephant
Six blind men were asked to determine what an elephant looked like by feeling different parts of the elephant's body. The blind man who feels a leg says the elephant is like a pillar; the one who feels the tail says the elephant is like a rope; the one who feels the trunk says the elephant is like a tree branch; the one who feels the ear says the elephant is like a hand fan; the one who feels the belly says the elephant is like a wall; and the one who feels the tusk says the elephant is like a solid pipe.
This story depicts how we interpret the correct data with the wrong mindset. Not surprisingly, this data bias does not solely happen to average populations and data experts.
The Bill & Melinda Gate Foundation
In his book, “Thinking, Fast and Slow,” Kahneman illustrates this with the story of how the Bill & Melinda Gates Foundation, the Annenberg Foundation, Pew Charitable Trusts, and the U.S. Department of Education invested over $2 billion in the “Small Schools Movement” based on studies showing a disproportionately high number of high-performing schools were small schools. It made intuitive sense that smaller schools would produce more successful students, and the data backed that intuition.
The problem is that a disproportionately high number of low-performing schools were also small. But since our minds are prone to disregard facts that don’t fit our narratives, billions were spent on this cognitive illusion. Small schools are not inherently better than larger ones.
Sometimes, we would never be able to see the elephant as a whole. As I mentioned, humans make sense of uncorrelated data and evidence. What if the elephant is too big and beyond our ability to interpret?
That’s why we need the courage to innovate.
Book to read: "Thinking, Fast and Slow" ------ Daniel Kahneman
”Give and Take” ------ Adam Grant