HKS Authors

See citation below for complete author information.

Co-Director, Women and Public Policy Program
Albert Pratt Professor of Business and Government


Small samples negatively affect the quality of the information we use when making group-based estimates. Small samples have higher variability than large samples, so data about a handful of female and minority leaders are less informative than the data about the large cohort of their male and white counterparts. While reliance on group level characteristics, i.e., stereotypes, is in itself hotly debated, the problem of stereotyping is compounded by the data being less accurate and less reliable the smaller the group is. Simply put, if you want to learn about the typical attributes of, say, millennials, a sample of 10,000 will yield more useful information than a sample of 100. In addition, relative to majorities, minorities are more likely to be subject to tokenism, and additional scrutiny, in numerically skewed groups where they make up only a small proportion of the group. People are unlikely to correct for small-N statistics and often erroneously consider small samples to be equally representative of the underlying population as large samples. Obviously, increasing the sample size would solve the small-N problem. As this is not always possible, another way to counteract the threat of inaccurate stereotypes is to increase the availability of role models by making visible individuals from underrepresented groups who are representative of the group as a whole. Additionally, changes in decision processes that decrease the impact of stereotypes on people’s judgments by focusing attention on individual-level data rather than group level characteristics are likely to improve diversity because differences in sample size no longer matter


Bohnet, Iris, and Siri Chilazi. "Overcoming the Small-N Problem." University of Massachusetts, Amherst, Center for Employment Equity, May 2020.