The law of small numbers is a cognitive bias where people show a tendency to believe that a relatively small number observations will closely reflect the general population. This is an extremely deceptive outlook and while it might not seem easy to spot at first glance those that take the time to understand the law of small numbers in sports betting and how they work can really get a good handle on the concept and avoid falling in to the trap it creates. Here is a look at the law of small numbers in sports betting and how to avoid falling in to its trap.
The Hospital Quiz
One of the best examples of the outlook for the law of small numbers is something referred to as The Hospital Quiz, which references a 1974 study by two psychologists named Daniel Kahneman and Amos Tversky. These two psychologists created a scenario where a certain town is served by two hospitals. In the larger hospital there are about 45 babies born easy day and in the smaller hospital there are about 15 babies born each day. As we know, about 50-percent of all babies are boys but that number will vary from day-to-day so sometimes it will be more than 50-percent and sometimes it will be less than 50-percent. For a period of one year, each hospital recorded the days in which more than 60-percent of the babies born were boys. Which hospital do you think recorded more such days between the large hospital and the smaller hospital? While 88-percent of those that answered the question said the larger hospital, the correct answer is actually the smaller hospital. The number of days in which the boys born outnumbered the girls born by at least six to four was nearly three times greater in the smaller hospital compared to the bigger hospital. Why is that? According to the binomial theory, a larger sample size is less likely to stray from 50-percent than a smaller sample size. Therefore the higher the number of babies born the less likely it is there will be a variation straying away from the 50-percent rule.
Translation To The Law Of Small Numbers
Kahneman and Tversky described the error made by its test subjects in their prediction as a belief in the law of small numbers. They believed that judgments made from small samples are often inappropriately perceived to be representative of the wider population. Conversely, a small sample demonstrating an apparently meaningful pattern will cause the observer to believe that the population will display the same meaningful pattern. In this case, the assumption would lead to a bias. The experience of perceiving patterns or meaningless data is called apophenia.
The law of small numbers is a cognitive bias where people show a tendency to believe that a relatively small number of observations will closely reflect the general population. Small numbers can sometimes be quite large but it’s important to understand how they exist in relation to the bigger and often more accurate sample size. People favor certainty over doubt and therefore are more likely to expect to see a pattern than completely random numbers. It’s important to appreciate the difference and understand the presence of the law of small numbers in sports betting and how it can lead to certain biases that should be accounted for when it comes to handicapping trends and advice.