The American Chemical Society recommends that undergraduate programs provide students with a curriculum which emphasizes the significance of statistics to the chemistry enterprise. Despite this recommendation, few chemistry or forensic science programs provide undergraduate level courses in statistics (including chemometrics) or data management. Most departments rely on the (sophomore-level) quantitative analysis and (junior-level) physical chemistry courses to introduce students to the statistical analysis of scientific measurements. To a lesser extent, general chemistry and instrumental analysis courses introduce and incorporate statistics as one, often secondary, component to an already exhaustive list of learning objectives. In the quantitative analysis and physical chemistry courses, students routinely generate and manipulate data sets, determine normal distributions, uncertainties, variance, linear regressions, and limits of detection/limits of quantitation. Typically, the teaching format contains a work-up template via a single lecture or handout that students copy each lab period, or whenever a new data set is generated. The consequence of such a recipe-driven exercise is that students perform the calculations with mechanistic efficiency, but gain little understanding of the scientific implications and real-world applications surrounding the statistical results. In this presentation, we will discuss our efforts to provide discipline-relevant statistics coupled with basic programming to assist students in interpreting and managing acquired data sets. Incorporating the free and open source program R for all statistical calculations coupled with the context of a QA/QC environment, students will become experts in organizing, manipulating, and visualizing small- and large-scale data sets. Demonstrating a thorough ability to collect and gather data from both internal and external sources, students will explore real-world applications of statistics in an accredited laboratory environment.

Introducing big data, statistics, R programming, and QA/QC in the analytical chemistry curriculum

255th National Meeting (2018)

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