Abstract
John von Neumann emphasized the importance of models in science. In this paper we compare the efficacy and ease of use of two quite different models, Benford’s Law and a version of Zipf’s Law to help us to understand the data that have rained upon us from the COVID pandemic. We conclude that Zipf’s Law seems to have much to offer. We recommend it and urge others to try it out. Benford’s Law, not so much.
Additional information
Notes on contributors
Paul Velleman
Paul Velleman is emeritus professor of statistics and statistical sciences at Cornell University. He is coauthor of six statistics textbooks (Intro Stats, Stats, Data and Models, and others, published by Pearson). He developed the DataDesk statistics software (www.datadesk.com) and DASL data and story library of teaching data sets (www.dasl.datadescription.com), both currently maintained and supported by Data description, Inc., where he serves as president and senior scientist. He is a fellow of the American Statistical Association and American Association for the Advancement of Science. He is also founder and president of Affordable Advanced Analytics (www.a3giving.org), a nonprofit company helping other small nonprofits with predictive modeling of their data. Velleman lives in Camden, Maine, with his wife and two dogs, practices Tai Chi, and sings with the a cappella group VoXX.
Howard Wainer
Howard Wainer is a statistician and author who has written Visual Revelations continuously since 1990. He has won numerous awards and is a fellow of the American Statistical Association and American Educational Research Association. His most recent book, co-authored with Michael Friendly, is A History of Data Visualization and Graphic Communication, which was published by Harvard University Press in 2021. Before this was Truth or Truthiness: Distinguishing Fact from Fiction by Learning to Think like a Data Scientist (Cambridge), which was named by the Financial Times to its “Top Six Books of 2016.”