Professor, Department of Statistics & Actuarial Science
Goodness-of-fit Testing, Inference on Stochastic Processes, Large Sample Theory
Analyzing a data set and getting answers that you like is gratifying, but how can you be sure your analysis is actually working? Statisticians tackle this question; they want evidence of how well an analysis works, and to obtain answers, they analyze methods of analyzing data. Dr. Lockhart is not fundamentally a data analyst; he does analyze data, but the focus of his research is to describe how well various methods are expected to work in different circumstances.
What is the most satisfying aspect of the work you do?
I like mathematical problem-solving. As my Ph.D. supervisor used to say, “I am not interested in research, I am interested in understanding, which is different.” It is very satisfying when I finally feel that I understand how things work.
What research do you do in describing how well a group of data fits a statistical model?
Lots of analytical methods hinge on specific and technical mathematical assumptions about the way the data were generated. Those assumptions are sometimes checkable and sometimes not. ‘Goodness of fit’ is the process of developing statistical methods to ask whether the assumptions you rely on are reasonable. My job is to develop rules that will test those assumptions. Any statistics problem will have competing suggestions for methods and I compare them to highlight their strengths and weaknesses.