The ideal statistics curriculum?

This week I (Bob) was part of a workshop on the future of neuroscience education. I got to be part of a rockstar panel with Tari Tan (Harvard), Monica Linden (Brown), and Rosalind Segal (Harvard). The event was organized by Lique Coolen (Kent State).

Tari spoke about curricular goals for neuroscience, reviewing an SFN initiative to define core competencies for trainees at the undergraduate and graduate level. I didn’t know about this! Tari gave a great overview; it’s well worth checking out: https://www.sfn.org/careers/higher-education-and-training/core-competencies

Monica discussed the role of generative AI in the future of neuroscience education and gave lots of thoughtful examples and resources.

Rosalind discussed developing an internship program for the neuroscience *PhD* program at Harvard! Very thougtful and interesting way for grad students to explore the diverse career paths that can come out of doctoral training — but also raised lots of interesting issues (a stated goal of internships that don’t impede research seemed hard to balance; students seemed to express worries about losing faculty support if they expressed ‘wrong’ career goals… lots to think about!).

My talk was about the ideal statistics curriculum. My short answer was “There isn’t one!” — neuro is too diverse and there is not only one ‘right’ way to do statistical inference. Still, the field of neuroscience shows some evident difficulties making valid claims from data (on that note, check out Chen et al., 2024), and so at least thinking about some broad ideals for training seems like a useful exercise. I came up with these guiding principles:

• Estimation thinking at the forefront
• Teach using simulations — for exploration at first, but eventually for planning experiments before they are conducted
• Quickly graduate from toy examples to real, complex data sets and projects that require critical thinking about Multiplicity and Interdependence, and teach robustness checking to help students validate their approaches.
• Integrate Open Science throughout
• Ensure training is not just about statistics, but about the ‘neglected factors’, including good design and measurement — strength of evidence and quality of research are about so much more than just the statistics generated, and improvements are about so much more than sample size.

My talk and some resources on each of these topics are here: https://osf.io/muy6u/wiki/Workshops%20-%20SFN%202024/

We’re collating all the resources from all the speakers; I’ll post a link when I have that.