The best thing about being a statistician is that you get to play in everyone else's backyard.
— John Tukey
All models are false, but some are useful.
— George Box
The scientist is not a person who gives the right answers, [but] one who asks the right questions.
— Claude Levi-Strauss
Statistics is the science of learning from data, of identifying and managing uncertainty, and of using data to make good decisions in an uncertain world. As the quote above from John Tukey suggests, Statistics and statistical thinking are critical tools in a wide range of fields, including agriculture, astronomy, biology, computer science, demography, economics, education and learning science, engineering, finance, insurance, manufacturing, marketing, medicine and public health, politics, psychology, science, and sports.
Statisticians use data to solve problems. This involves devising methods for properly analyzing the data, building mathematical models for interpreting the results and determining the strength of the conclusions that can be drawn from them, and clearly communicating those results so that they can have a due impact. Statisticians must bring to bear a wide variety of skills in computing, mathematics, writing, and scientific practice. Among the most important of these are the willingness to question assumptions and an eagerness to learn about the subject-matter context in which methods will be applied. The body of tools and knowledge involved in Statistics opens up many fascinating directions of inquiry and research.
The Statistics Department at Carnegie Mellon University was founded in 1966, then one of the few existing, independent statistics departments in the country. Though small at the time, the department was a fertile training ground for a generation of outstanding statisticians. Over the years, the Department built a world-class reputation as a leader in Bayesian statistics, cutting-edge computational methods, and intensive interdisciplinary research.
The Department's reputation has grown steadily in the ensuing years and continues to be well known for its contributions to theory, methods, and practice. We have particular strengths in machine learning, neuroscience, astronomy, genetics, public policy, network methods, nonparametric methods, privacy, and educational statistics.