# Square root biased sampling

Square root biased sampling is a sampling method proposed by William H. Press, a computer scientist and computational biologist, for use in airport screenings. It is the mathematically optimal compromise between simple random sampling and strong profiling that most quickly finds a rare malfeasor, given fixed screening resources.[1][2]

Using this method, if a group is ${\displaystyle n}$ times as likely as the average to be a security risk, then persons from that group will be ${\displaystyle {\sqrt {n}}}$ times as likely to undergo additional screening.[1] For example, if someone from a profiled group is nine times more likely than the average person to be a security risk, then when using square root biased sampling, people from the profiled group would be screened three times more often than the average person.

## History

Press developed square root biased sampling as a way to sample long sequences of DNA.[3] It had also been developed independently by Ruben Abagyan, a professor at TSRI in La Jolla, California, for use in a different biological context.[4][5] An even earlier discovery was by Martin L. Shooman, who used square root biased sampling in a test apportionment model for software reliability.[6]

Press' later proposal to use square root biased sampling for airport security was published in 2009.[1] There, he argued that this method would be a more efficient use of the limited resources possessed for screening, as compared to the current practice, which can lead to screening the same persons frequently and repeatedly.[2][3] However, use of this method presupposes that those doing the screening have accurate statistical information on who is more likely to be a security risk, which is not necessarily the case.[7]