My goal is to improve the practice of statistics. Many statistical procedures can be justified in theory only when they employ large sample sizes. My studies of small sample characteristics range from giving rules for applying a chi-squared test in contingency tables to establishing confidence limits for arbitrary nonlinear regression functions.
Experimental design is fundamental to the practice of statistics.
Supported by a National Science Foundation grant, Kathryn Chaloner and I are applying the principles of optimal design to nonlinear problems. Applications include determining the dilution levels for blood tests, establishing ingredient mixtures in animal feed to optimize growth, and designing stress tests for electrical components. Development of optimal experimental designs for these situations depends heavily on intensive computing methods, which have only recently become available. With future improvements in computing power, optimal design procedures will become standard statistical practice. In the last two decades federal agencies have begun using experiments to study social problems.