Data-driven decision making in an introductory physics lab

Data-driven decision making in an introductory physics lab

A new lab activity for first-year introductory physics teaches students to use data to drive decision-making in science and engineering processes. Using the popular PDCA (plan-do-check-act) cycle, students manufacture a small sample of ball bearings out of modeling clay. By statistically analyzing their sample, they determine whether a larger shipment will meet tolerance levels specified by the lab TA. They then make decisions on ways to change their manufacturing process to improve results, employing another round of data analysis to confirm whether the change improved their process. Judging by student comments, such an activity reinforces the conceptual basis for numerous statistical properties, helps distinguish many commonly confused statistical concepts, and reinforces the use of data in process management. This activity can be incorporated into either algebra-based or calculus-based physics labs and, because it does not rely on background knowledge of physics concepts, should prove ideal for the early weeks of lab instruction.

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Patrick Talbot and Michael Walkup are Fresno State students.

Walkup, J. R., Key, R. A., Talbot, P. R. M., & Walkup, M. A. (2019). Data-driven decision making in an introductory physics lab. American Journal of Physics, 87(8), 654–659. https://doi.org/10.1119/1.5100946