Insensitivity to sample size is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic without respect to the sample size. For example, in one study subjects assigned the same probability to the likelihood of obtaining a mean height of above six feet [183 cm] in samples of 10, 100, and 1,000 men. In other words, variation is more likely in smaller samples, but people may not expect this.

In another example, Amos Tversky and Daniel Kahneman asked subjects

A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower.
For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days?

  1. The larger hospital
  2. The smaller hospital
  3. About the same (that is, within 5% of each other) 

56% of subjects chose option 3, and 22% of subjects respectively chose options 1 or 2. However, according to sampling theory the larger hospital is much more likely to report a sex ratio close to 50% on a given day than the smaller hospital (see the law of large numbers).

Relative neglect of sample size were obtained in a different study of statistically sophisticated psychologists.

Tversky and Kahneman explained these results as being caused by the representativeness heuristic, according to which people intuitively judge samples as having similar properties to their population without taking other considerations into effect. A related bias is the clustering illusion, in which people under-expect streaks or runs in small samples. Insensitivity to sample size is a subtype of extension neglect.