Small samples mislead us about the world. Human stories born from the fluctuations of probability.

Small samples mislead us about the world. Human stories born from the fluctuations of probability.

The law of small numbers is a cognitive bias where people interpret results from small sample sizes as representative of the whole. Despite the large random variation and tendency for extreme outcomes in small samples, people seek meaning and patterns there. It's natural for dice rolls to show bias early on, yet we often mistake chance for inherent cause or property. Underlying this is a psychological aversion to uncertainty and a desire for explanations and narratives. A few striking results stick in the memory, reinforcing false convictions. Behavioral economist Daniel Kahneman highlighted these intuitive errors, emphasizing the importance of considering sample size and margin of error. The wisdom to avoid being swallowed by tales of chance lies in not rushing to conclusions with small data, but taking a step back and maintaining skepticism.