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Matt Russell of Etna is a forest analytics consultant at Arbor Custom Analytics LLC.
“Statistics is the grammar of science.” It’s a late 19th century phrase attributed to Karl Pearson, one of the world’s first statisticians. In Pearson’s time, the quote related to the philosophy of science and how it began the nascent field of statistics. Today, the quote speaks to how statistics is a universal prerequisite in science and research.
Find two random articles: say one from the New England Journal of Medicine and the other from Nature Climate Change. The topics will be completely different, but scan the methods sections in the articles and you will no doubt find the same “scientific grammar” in each. Anyone with a course or two in statistics will be familiar with the hypothesis tests, standard deviations and error bars surrounding bar graphs found in both journals.
A solid education in statistics, broadly defined as the science of learning from data, is critical for anyone entering today’s workforce. With a world increasingly being driven by data, the ability to separate a signal from noise is essential to drawing conclusions in academic research and making decisions in industry and government. Statistical principles also serve as the foundation of many tools used in artificial intelligence and machine learning, such as ChatGPT. If we seek to use AI tools to advance our society in responsible ways in the future, we should prioritize data literacy in all of our students, from K-12 classrooms through graduate student seminars.
In Maine, the need for a statistical education is especially needed for tomorrow’s scientists in health care, natural resources and energy, among other sectors. Access and affordability of health care requires a thorough understanding of medical research in combination with knowledge of social issues affecting those with unequal access to treatment. Forecasting what Maine’s forests will look like in the future amid a changing environment requires analytical skills to gather large volumes of data and use modeling techniques to assess how forests will change.
The University of Maine should serve as a factory of graduates with skills in data analysis and statistics. However, courses and degree programs in statistics are scant at UMaine relative to similar-sized land grant universities in other states. UMaine does not currently offer a bachelor’s degree in statistics, only a minor. At the graduate level, course offerings are so limited that a student attempting to earn a concentration may not be possible.
Not having a strong program in statistics and the quantitative sciences also comes to the detriment of other graduate students in non-statistics majors. Graduate students in natural resources, agriculture, health care and other programs require an understanding of statistics to apply quantitative methods to their own research projects. This is typically gained through a course or two in statistics. But without a robust statistics program that offers courses on the latest statistical methods, the quality of graduate student research suffers and a workforce of future scientists lack the quantitative skills that future employers expect.
With last year’s designation as a Carnegie R1 Top-Tier Research University, UMaine has a responsibility to increase its commitment to data literacy. This includes increasing its offering of statistics and quantitative science courses and programs across campuses, conducting research that integrates statistics and data science principles with AI, and investing in faculty that lead these efforts. This will help UMaine produce a more data literate workforce for the state in addition to drawing the national and international recognition that the university deserves.
Investing in the statistics and the data sciences will help prepare Maine’s future scientists and researchers. Data and information is said to be the new oil of the 21st century, and Maine needs to produce highly skilled researchers and scientists that help companies and organizations address the state’s future challenges.
