By Adan Alter*
The law of small numbers is a bias in human judgment: It describes situations in which we too readily conclude that a pattern we see in a small sample of information also holds for a much larger sample.
Imagine a fair coin that is tossed three times. You will have a one-in-four chance of turning up a string of three heads or tails. If you make too much of that small sample, you might conclude that the coin is rigged.
If you continue to toss the fair coin, say, 1,000 times, you are far more likely to turn up a distribution that approaches 500 heads and 500 tails.
As the sample grows, your chance of turning up an unbroken string shrinks rapidly.
A string is far better evidence of bias after 20 tosses than it is after three tosses – but if you succumb to the law of small numbers, you might draw sweeping conclusions from even tiny samples of data.
The law of small numbers explains a range of harmful behaviors: stereotyping (believing that all people with a particular trait behave the same way), relying on first impressions (concluding from one encounter or interview that someone is smart, capable or trustworthy) and basing financial decisions on transitory, short-term patterns in a market, such as one day’s uptick in a stock.
The solution to this problem is to pay attention not just to the pattern of data but also to how much data you have. Small samples aren’t just limited in value; they can be counterproductive because the stories they tell are often misleading.
Dr. Alter is a psychologist at New York University’s Stern School of Business and the author of “Drunk Tank Pink” and “Irresistible.” This article is re-posted from edge.org and is here with permission.