Our minds see a lot of patterns that don’t exist. We make observations of randomness and find patterns that we assume to be based on a causal link when in reality no causal structure exists between our observations. This can happen in 3 point shooting in basketball, in observations of bomb locations in WWII London, and in music streaming services. We are primed to see patterns and causes, and we can construct them even when we shouldn’t. One contributing factor for incorrect pattern observation is that we tend to make post hoc conclusions, making observations after the fact without predicting what we might expect to see before hand.
Using the WWII example, Cass Sunstein and Richard Thaler in the book Nudge show how people developed misconstructions of German bombing patterns in London during the war. The German bombing wasn’t precise, and there was no real pattern to the bombing raids and where bombs actually exploded across the city. Nevertheless, people mistakenly viewed a pattern in the random distribution of bombs. The authors describe the mistaken pattern identification by writing, “We often see patterns because we construct our informational tests only after looking at the evidence.”
People could map where bombs fell, and then create explanations for what targets the Germans were aiming at, for why the Germans would target a certain part of the city, and what strategic purpose the bombing was trying to accomplish. But these reasons are all post hoc constructions meant to satisfy a non-existent pattern that someone expected to find. We also see this in basketball, when a shooter makes a few baskets and is believed to have the hot hand or be on fire. In music streaming services, algorithms are actually tweaked to be less random, because listeners who hear two consecutive songs or more by the same band will assume the streaming isn’t randomizing the music, even though random chance will sometimes pick a string of songs from the same band or even from the same album.
The examples I mentioned in the previous paragraph are harmless cognitive errors stemming from poorly constructed post hoc explanations of phenomena. However, post hoc conclusions based on non-existent patterns are important to consider because they can have real consequences in our lives and societies. If we are in a position to make important decisions for our families, our companies, or our communities, we should recognize that we possess the ability to be wildly wrong about observed patterns. It is important that we use better statistical techniques or listen to the experts who can honestly employ them to help us make decisions. We should not panic about meaningless stock market fluctuations and we should not incarcerate people based on poor crime statistic understandings. We should instead remember that our brains will look for patterns and find them even if they don’t actually exist. We should state assumptions before we make observations, rather than making post hoc conclusions on poor justifications for the patterns we want to see.