Statistical thinking is pervasive in all disciplines engaged in empirical inquiry. The purpose of Statistical Science is to develop methods for designing and analyzing such inquiries, and to disseminate this methodology through teaching and scholarly communication.
Development of useful statistical methodology cannot take place in a vacuum. To be scientifically relevant this development should be problem-driven, motivated and guided by applications of scientific importance. Identifying and understanding important applications requires interaction with other disciplines that acquire and analyze data. Collaborative research is therefore essential to the viability and growth of Statistics.
While mathematical tools provide the machinery for theoretical analysis of statistical procedures, Statistics is not a subfield of Mathematics. In the last 30 years, data collection and data analysis have been transformed by the computer revolution. Computers have made it feasible to collect and analyze large data sets (petabytes of information or more).
Demand is rapidly increasing for new data analytic tools, and for individuals trained to invent, evaluate, and apply them. The increasing importance of computers in both data collection and data analysis has made expertise in computing an important prerequisite for creating and applying new and innovative data analysis tools.