Clustering bias, sometimes referred to as the clustering illusion, is when traders or gamblers believe they see significant patterns or clumps within data. The patterns or clumps they are seeing, however, are entirely random. Unsurprisingly, it has its roots in gambling, with gamblers who believe they are ‘beating the system’.
It’s believed that clustering bias comes from the fact that it’s human nature to recognise both people and things based on their overall pattern, rather than every detail. Although this is highly useful, it does mean humans have a tendency to see patterns in places they don’t actually exist.
One of the most famous examples of clustering bias occurred during the second world war. The German army launched several attacks throughout south London, with many neighbourhoods seemingly targeted by the German bombs. Other neighbourhoods, however, remained untouched by the bombs. This resulted in several people believing that the areas which weren’t hit were in fact home to German spies. Once the data was thoroughly analysed after the war, it was found that the hits had been distributed throughout south London entirely randomly. This is the same illusion that affects traders today; by seeking out a pattern that isn’t there among data, they mistakenly believe that it must be caused by something. The fact is that, because there is no pattern, everything is entirely random and is caused by nothing.
The cause of the cluster
If, for example, you were to argue that a cluster was based on something – the same way someone might argue that a road accident was caused by the installation of some distracting lights nearby – then it’s a good idea to think about the Texas sharpshooter fallacy. The fallacy, which is inspired by a story about a Texas marksman, states that a person travelling through Texas comes across a barn. They notice that the barn has a few bulls-eyes painted on the outside and each bulls-eye has several bullet holes close to the centre. After noticing the holes, the traveller approaches the owner of the barn to ask them how they became such an accurate shooter. The Texan replies by telling the man they shot first and painted the bulls-eyes on afterwards. The morale of the story is that noticing a pattern first is easy. By noticing a pattern, you then go on to make an argument based on that pattern.
Another aspect that affects the clustering illusion was discovered by Daniel Kahneman and Amos Tversky. They came across a psychological bias which they referred to as a belief in a law of small numbers. The law of large numbers states that, if you’re conducting a random experiment, as the number of trials increases, you will start to see the results approach a value that’s expected. The belief in the law of small numbers is merely a human error that occurs when people believe the expected value will be reached with a small number of trials. If a coin was tossed six times, the expected value is three tails and three heads. We mistakenly believe that a string such as HTHTHT stands a better chance of coming up than HHHHHH, despite the fact any individual string has as much chance of coming up as any other. This is a psychological bias and is not based on anything other than a misplaced belief in patterns.
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