Jaya M. Satagopan is professor in the department of biostatistics and epidemiology at Rutgers School of Public Health and a full member of the Cancer Prevention and Control Program at Rutgers Cancer Institute of New Jersey. She completed her PhD in statistics from the University of Wisconsin-Madison and an MSc in science communication and public engagement from the University of Edinburgh.

During travels or at social events, I inevitably run into someone asking me about my profession. When I say I am a statistician, the reactions often include the following:

 
Such comments are despite the ubiquitous nature of statistics in our lives.

We constantly engage with data in numerous forms—checking time, commuting distance, education, national and international policies, sports, entertainment, health decisions, phone calls, internet use, and more. Statistics—which is the science of collecting, analyzing, and interpreting data—occurs naturally in our daily lives. Yet, the term statistics as a profession elicits the above comments. These and many other personal experiences continue to influence my efforts to communicate statistics in my introduction to biostatistics class.

Over the years, I have come to appreciate that enthusiasm for statistics varies. Therefore, I resort to diverse strategies when explaining the results of my statistical analyses and research to my collaborators from other disciplines. Some prefer a quick message about the outcome of a hypothesis test for a project’s primary aim. Others favor detailed tables, figures, and perhaps an infographic. And some go over my summary alone for a few days and then reach out for discussion. People process statistics in many ways, with different tools, and in different environments.

This understanding stood out for me when I was invited to develop an introductory statistics course for early-career cancer biomedical researchers. How do I communicate statistics to an audience from a different discipline, especially when I know people process statistics in different ways? I did not have an answer. But I decided to give it a try using my personal encounters with data as the building blocks for this effort. Today, I adapt this approach for my class.

I learned how statistics shapes and is shaped by other disciplines while a graduate student in the department of statistics at the University of Wisconsin-Madison. I especially understood many ideas behind experimental design have their roots in agriculture. The Monday evening seminars organized by George Box demonstrated the significant role of experimental designs in the automobile industry. As a nod to this history, I use agriculture data on corn yield, the motor trend car road tests data set, and the New York transportation fuels data from Kaggle to motivate types of variables, statistical summaries, data visualization tools, and hypothesis testing in my class.

It is over casual chats with friends pursuing graduate degrees in medicine and public health that I learned Florence Nightingale, the founder of modern nursing and a major public health figure in the 1800s, was also a statistician and proponent of data visualization. The book racks of Steenbock Library for Agriculture and Life Science and Memorial Library revealed many other gems, including Janet Lane-Claypon, an English epidemiologist who pioneered the use of case-control and cohort studies, and Edith Abbott, an economist and statistician who wrote a groundbreaking report in 1915 on crime statistics in Chicago. The lives and works of these women are particularly relevant in a (bio)statistics course for researchers in public health and medicine. I incorporate videos, blog posts, and podcasts about these prominent women to provide a refreshing learning environment.

After moving to the East Coast, rail commute highlighted the foundational role of statistical uncertainty in my daily life. How likely is it to rain today in New York City? The newspaper reports 75 percent chance of rain. Does it mean it will rain in 75 percent of the area of New York City? Or will it rain today in New York City for 18 hours, which is 75 percent of 24 hours? Or will it be raining 75 out of 100 times I step out? Or will there be rain 75 times out 100 on days like today? Why is a percentage so confusing? Podcasts offer unprecedented opportunities for in-class conversations to better understand statistical terms used in our daily life. Several episodes of the BBC Radio 4 podcast, More or Less, are integrated into many topics in my course, including discussions about chance and percentages.

Rail commute also introduced me to meetups such as R Ladies NYC, opening the door for nonfiction books. Of note is Dear Data, a book of 52 postcards by Georgia Lupi and Stefanie Posavec describing their lives in data and highlighting how everyone can become a data collector. When a student approaches me expressing a fear of statistics, I encourage them to read at least some of the postcards in this book. The response has been overwhelmingly positive, with students reporting more confidence in engaging with statistics courses.

How a course is structured is another dimension of statistics communication. Two years ago, Sujata Patil—currently at the Cleveland Clinic Foundation—and I started to develop an introductory statistics curriculum with National Institutes of Health support. Recognizing that people consume data and process statistics in different ways, we planned a curriculum using the flipped classroom approach that allows one to learn at their own pace, with a significant portion also taking the form of “learn by doing.” Flipping the classroom has allowed me to incorporate several personal experiences with data—summarized above and also available through the ASA’s Section on Teaching of Statistics in the Health Sciences blog—into my course materials at Rutgers School of Public Health.

Data is fundamental. A statistics course offers the conceptual foundations in quantitative reasoning to extract meaningful information from data in a responsible manner. But many—in social settings and in the classroom—find statistics daunting. To allay these fears and promote data and statistical literacy, I would like to issue a call to action for communicating statistics using data that are part of our daily lives.

For course materials, visit the ASA’s Section on Teaching of Statistics in the Health Sciences blog.