Citation (IEEE): T. P. Davis, “Science, Engineering, and Statistics,” Applied Stochastic Models in Business and Industry, vol. 22, no. 5–6, pp. 401–430, 2006. doi: 10.1002/asmb.643.
What it argues
Drawing on a long career in automotive quality engineering, Davis argues that engineering failures usually stem from inadequate understanding of the underlying science, and that statistics is most valuable when it is used to build and test that understanding rather than to analyze data after the fact. He connects designed experiments, robustness and failure-mode avoidance, and urges engineers to think about variation and noise from the start of design.