Be aware of your biases and use data to confirm your beliefs. Be ready to change if the data doesn’t align.
One story that keeps fascinating me when it comes to science is how people in the early 20th century insisted on the existence of aether for transferring light. The general belief was that similar to how sound was mediated by air, there was another medium that would allow light to flow. This was important to explain how light could travel from, for instance, the sun to the earth, even though there was no air. Many research articles were written describing the properties of aether and how it interacted with light. Only to be proven wrong a few years later by research that showed that light travels perfectly well through a vacuum and requires no medium.
Likewise, I’m always amazed by the intricate models I occasionally encounter in a history or art museum that explain how the ‘lights in the sky’ moved around the earth in rather strange patterns. We all tend to laugh and make fun of those silly people that didn’t realize that the earth rotates around the sun. However, it’s important to remember that in a few decades or a century, people will make fun of us because of our silly explanations that, in their view, make about as much sense as the sun circling the earth.
During my life, I’ve time and again managed to create stories for myself that, in hindsight, turned out to be complete bullshit. I already told you about how the software architecture community in the 1990s believed that it was virtually impossible to change the architecture of a system after development was started. Such fabrications can be found in all parts of our lives – personal, societal and professional.
During the 20th century, several isms were tried out at a societal level based on storytelling with disastrous consequences. Fascism resulted in tens of millions of deaths around World War II. Even worse, communism resulted in an estimated death toll of well over 100 million during the century. The proponents of these isms often claim that this was due to the poor execution of the principles, but in reality, human nature will cause the same outcomes when these isms are tried again.
As storytelling machines, we excel at creating stories that provide a level of sense-making. The problem is, as the previous examples indicate, that telling the wrong stories to ourselves and the people around us isn’t free of consequences. Instead, it can lead to terrible outcomes.
We need to ensure that the stories we tell have a solid grounding in reality and the best way to do so is to use data. In essence, this is what science is all about. We create stories, typically referred to as hypotheses, that are then translated into experiments designed to either prove or disprove the hypothesis.
Many question science because an explanation considered true at one point in time can later be displaced with a different and hopefully better one. This is a fundamental misunderstanding of the role of science; we intend to develop the best explanations about phenomena, but the process of developing better, more accurate explanations is what science is all about. I just wish that many so-called scientists in the media were a little more humble and less absolute in the way they present their theories to the general public.
My point is that we need to take a scientific approach to our work and lives as well. Rule #4, question everything, is about not automatically accepting stories told by others or by ourselves. It focuses on saying no to bad explanations. Here we’re concerned with creating and saying yes to the best explanations we can come up with and using data to validate them.
The challenge we often run into is that our feelings or opinions say one thing while the data says something else. We experience this cognitive dissonance as uncomfortable and tend to resolve it by dismissing the data or by generating an alternative explanation that allows us to keep our views and opinions. This is exactly the wrong thing to do. We need to accept that we may be wrong, or likely are wrong, and start to generate alternative explanations that we can hopefully test and validate with data. This is at the heart of growth, intellectual and otherwise, as growth automatically means that we’re changing. And changing opinions is of course the main change we need.
A complicating factor is that the human brain has a quite limited information processing capability. As a result, something like 99.9 percent of all information reaching our senses is ignored before we even become aware of it. An illustrative example is when you buy a new car. The moment you’ve made the decision, you suddenly see cars of the same model and color all the time whereas you didn’t notice them earlier. The brain is primed to see what it considers relevant and ignores everything else.
The risk is of course that if we’re unaware of these biases, we tend to believe that we’re experiencing reality as it is, while, in fact, we’re seeing the world through highly selective and distorting glasses. Rather than believing everything we observe is ‘the truth,’ our job is to continuously work on removing as much of the distortion as possible and ensure that we focus as much as possible on the things that indeed are the most valuable. Data is the most effective mechanism to prove or disprove our thoughts, but in my experience, also reflection, introspection and meditation can be very helpful in this process.
We’re exceptionally good at creating tales that explain the world around us in ways that align with our beliefs. The problem is that quite often these explanations and stories are inaccurate. This may seem innocuous, but it can result in significant harm to you, yours and society. Instead, be aware of your biases and use data to confirm your beliefs and be ready to change if the data doesn’t align with your current set of beliefs. Growth comes from changing for the better, not from holding on to what you believed yesterday. As Patrick Gelsinger said: “Data is the new science. Big data holds the answers. Are you asking the right questions?”