International Statistical Engineering Association

What is Statistical Engineering?

In a quick nutshell, statistical engineering is the discipline dedicated to the art and science of solving complex problems that require data and data analysis. The more challenging of these problems truly keep senior organizational leaders awake at night. These problems almost always are unstructured and typically large, crossing several disciplines. These problems typically come unstructured and thus usually look like a “mess.” The key is how to provide enough definition and structure to create a reasonable solution path to a truly sustainable solution.

Our discipline provides guidance to develop appropriate strategies to produce sustainable solutions. It discusses which statistical and analytic tools/methods are appropriate depending on the circumstances, and it outlines how to create sustainable solutions efficiently and effectively. Ultimately, statistical engineering is the discipline that helps practitioners determine “the right tool for the right job at the right time, properly applied.”

Real problems, particularly large, complex, unstructured problems, are not so simple. There is no single “correct” method, and in most cases multiple statistical methods and perhaps multiple disciplines are needed. In other words, a sequential approach is needed:


Similarly, there is no “cookbook” that will lead practitioners step by step through successful solution of all such problems. However, there is a framework to give some guidance as to how to think about approaching such problems. The figure below, based on Hoerl and Snee (2017), shows the phases that statistical engineering projects typically go through.


Core Processes Utilized in Statistical Engineering 

  • Data acquisition, including surveys and experiments
  • Data exploration and visualization
  • Model building
  • Drawing inferences (learning) from models
  • Solution deployment and sustainability

Benefits of Statistical Engineering

  • More effective problem solving
  • Discipline that can be learned, utilized and enhanced
  • Better, more effective use of data
  • Sustainability of solutions and results
  • Broader understanding and utility of statistical thinking and methods

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