In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist
Ronald Fisher was by all accounts a first-rate mathematician, but he saw himself as a scientist, not a mathematician, and he railed against what George Box called (in his Fisher lecture) "mathematistry". Mathematics is the indispensable foundation for statistics, but our subject is constantly under assault by people who want to turn statistics into a branch of mathematics, making the subject as impenetrable to non-mathematicians as possible. Valuing simplicity, I describe ten simple and powerful ideas that have influenced my thinking about statistics, in my areas of research interest: missing data, causal inference, survey sampling, and statistical modeling in general. The overarching theme is that statistics is a missing data problem, and the goal is to predict unknowns with appropriate measures of uncertainty.
Biostatistics | Social and Behavioral Sciences
Little, Roderick J., "In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist" (January 2013). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 97.
Article based on Dr. Little's 2012 R.A. Fisher Lecture at the Joint Statistical Meetings