@proceedings{bibcite_422,
  author = {Aidan Montare},
  title = {When Life isn{\textquoteright}t Gaussian: The Allan Deviation Family of Statistics},
  abstract = {When analyzing data, it is common to assume a Gaussian distribution of noise around a "true"\ mean value. But real life often isn{\textquoteright}t Gaussian, so how do we deal with other kinds of noise? How do we think about data that does not have a well-defined mean? The Allan deviation family of statistics offers a series of tools to address these problems. Originally developed for characterizing the performance of oscillators, the family of statistics is now a mainstay of all kinds of time and frequency measurement and has found a growing range of applications across fields. In this presentation, I give a brief introduction to the Allan variance, highlight some other related statistics, and show their use in a variety of problem areas. I provide example code in Python and suggest a starting point for exploring these concepts with simulation.},
  year = {2024},
  journal = {HamSCI Workshop 2024},
  month = {03/2024},
  publisher = {HamSCI},
  language = {eng},
}