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Josh Warren: From clear, organized messaging comes a diversity of statistical usage

January 10, 2024
by Mike Honsberger

Spotlight on Teaching: Josh Warren, PhD, Associate Professor of Biostatistics

Biostatistics is a field that requires a precise form of creativity. Enabling students to embark on their own creative journey is the goal of Josh Warren’s teaching.

Warren, an associate professor of biostatistics, teaches Bayesian Statistics (BIS567) to a diverse group of students, including those from biostats, MPH, MS, and PhD programs, as well as individuals from Yale’s computer science and statistics departments. In his classroom, he underscores the widespread applicability of the statistical modeling that students use in this course.

Bayesian inference is a method of statistical inference where prior beliefs for model parameters can be incorporated into an analysis and updated once data are observed. The techniques for fitting models in a Bayesian setting are extremely flexible and allow for dataset-specific models, which may more accurately reflect underlying mechanisms and hypotheses, to be developed. It is extremely flexible, allowing students to adapt and tweak frameworks based on the unique characteristics of their data. Students often reach out to him years after taking his course to tell him about their applications in a vast array of public health scenarios.

“Being able to write down and ultimately make inference for the exact model that your dataset and research project requires is a major strength of working in the Bayesian paradigm,” Warren said. “Teaching students the skillset needed to achieve this freedom is a major goal of the course.”

As a student, Warren appreciated consistency and predictability in his studies. To this end, he focuses on delivering a clear and organized message throughout his course, with each lecture following a similar cadence from week to week. Students face broad variations in teaching styles as they navigate their time at YSPH. Warren’s approach resonates with many of his students, who find comfort in knowing what to expect in each lecture.

Being able to write down and ultimately make inference for the exact model that your dataset and research project requires is a major strength of working in the Bayesian paradigm. Teaching students the skillset needed to achieve this freedom is a major goal of the course.

Josh Warren

Warren sees enormous value in office hours. He tries to make them as accessible as possible by offering face-to-face meeting times every day of the week with a member of his teaching team. This way, he accommodates diverse schedules and ensures that students can find a time that suits them. This flexibility is particularly important for students with varied commitments, including work, research, and other academic responsibilities.

Flexible office hours is just one of the tools that he uses to develop intellectual independence in his students. He has designed his course so that the homework assignments both reinforce taught material and require independent problem-solving. The intention is to push students to their limits, encouraging them to apply their skills independently. Warren and his teaching fellows use their office hours to guide students in this exercise.

Warren uses his organizational skills to thoroughly prepare the material he will be presenting for each lecture, which reduces his anxiety around public speaking. After each lecture, he engages in detailed notetaking about how the class went. He evaluates aspects such as engagement, student comprehension, and overall effectiveness. He then uses this collection of notes to prepare for the course the following year. This internal feedback loop allows him to adjust based on his personal teaching style, the dynamics of each class, and his own evolving understanding of effective teaching practices.

During the semester, Warren considers himself the on-call expert on Bayesian statistics for his students – whether they want to discuss homework problems, have questions from lecture material, or wish to know how to apply Bayesian statistics to their research. It’s a time-consuming goal, but hearing the success stories as these students take their newly developed skills out into their careers makes it worthwhile.

“I receive emails from students, even years after they have taken my course, expressing how the knowledge they gained is practically useful in their current work,” he said. “Hearing how they have managed to express the statistical creativity I try to teach in class makes the effort I put in worthwhile.”

Whether you are a student or a professor, take a moment today to reach out to a teacher who has had this type of impact on you. Not only does it validate the effort that they put into their teaching, but it will help you make your mark on the educational systems that you travel through.

Submitted by Fran Fried on January 10, 2024