  Extreme value analysis (EVA) is a statistical method that studies the
properties of extreme values of datasets, crucial for fields like engineering,
meteorology, finance, insurance, and environmental science. EVA models extreme
events using distributions such as Fr\'echet, Weibull, or Gumbel, aiding in
risk prediction and management. This review explores EVA's application to
nanoscale biosystems. Traditionally, biological research focuses on average
values from repeated experiments. However, EVA offers insights into molecular
mechanisms by examining extreme data points. We introduce EVA's concepts with
simulations and review its use in studying motor protein movements within
cells, highlighting the importance of in vivo analysis due to the complex
intracellular environment. We suggest EVA as a tool for extracting motor
proteins' physical properties in vivo and discuss its potential in other
biological systems. While EVA's use in nanoscale biological systems is limited,
it holds promise for uncovering hidden properties in extreme data, promoting
its broader application in life sciences.
