Dr. Dooti Roy is a people leader, global product owner and a methodology statistician at Boehringer Ingelheim (she didn’t give me where she worked in her bio so she might not want this) who enjoys developing/deploying innovative clinical research and statistical visualization tools with expertise in creating and leading dynamic cross-functional collaborations to efficiently solve complex problems. She is currently focused on research and methodological applications of Bayesian statistics, artificial intelligence and machine learning on clinical efficacy analyses, patient adherence, and dose-finding. She is passionate about promoting diversity and inclusion, mentoring, cross-cultural collaborations, and competent leadership development. She unwinds with painting, reading, traveling and heavy metal.
Suzanne Thornton professor of Statistics at Swarthmore College, a liberal arts undergraduate-only institution. As an educator, she strives to teach students to understand statistics as the language of science and prepare them to become stewards of the discipline. In 2020 she chaired an ASA presidential working group on LGBTQ+ representation and inclusion in the discipline and earlier this year, she was appointed to a three year term to serve on the National Advisory Committee for the US Census.
Episode Description
Measurement accuracy is something all quantitative researchers strive for, as you want to make sure you're measuring what you want to be measuring. When it comes to gathering gender and sex data, though measurements are complicated, beyond simply teasing apart sex and gender, there's also the imperative to ensure the language and measurement tools researchers use are inclusive of all experiences. That's the focus of this episode of stats and stories with guests Dooti Roy and Suzanne Thornton.
+Full Transcript
John Bailer A few episodes ago, Rosemary and I got to sit down with Swarthmore colleges Suzanne Thornton and Behringer Ingelheim, Judy Roy to talk about their piece and significance magazine about how the changing understanding of sex and gender is making statisticians take a long look at their own data collection methods. We're happy to have them back again on this episode of stats and stories to continue this conversation. I'm John Baylor stats and stories is the production of Miami University's departments of statistics and media, journalism and film, as well as the American Statistical Association. I am joined again by my colleague and CO panelist Rosemary Pennington from the Department of media, journalism and film. So to start, rosemary, I think you had an experience early on trying to use more general inclusive language, right?
Rosemary Pennington
Yes, and I really and truly, I wish I'd had something like this when I was doing all that work early on in my PhD because I had people on my committee who were like, why are you even worried about this? And I'm like, because I want to make sure I'm being inclusive. And I'm getting what I want to get like, how is that not? Why are we having this conversation?
John Bailer
Oh, and I wish I could build a time machine and go back 30 years to some of the intro stack classes that I taught and also some that I did. And if you take it back a bit further, some of that I took the understanding and kind of the conversation about this. It just wasn't it wasn't on radar, right?
Suzanne Thornton
Yeah, I am so happy to have this opportunity to have this discussion with you and duty. And our goal, I think is to well I can speak for myself. My goal is to speak to researchers, people who are aware of, you know, gender diversity and diversity and sexual characteristics and to provide helpful recommendations from the perspective of somebody whose job it is to make sense of data of observable information and because I think there's plenty of interest but as duty you mentioned, there's this is just now kind of entering the stats conversation.
Dooti Roy
I mean, I think this conversation has should have been a staff conversation long time ago, but somehow it is not and we all felt that this was time we change it and you know, I didn't mention this but since Suzanne is a professor here, I also started teaching at at UConn since last year, I teach my own course there. So I basically see the value of telling students that story because there is such a power in in telling people don't just think about a topic when it comes to your own thing. We must think about what's around us and around in the world and being an ally is really powerful. So I love seeing people realize I could do more for a house. And that happens for the past two years with my students and this is also really, really encouraging and fruitful for me.
John Bailer
You know, I wonder if some of this is is part of the the sort of thinking and In some stack, sort of methods, classes, you know, the variables came to us, they, it's almost like they came down from they've been, you know, carved in stone, and we take these, and we just do a bunch of stuff with it without without ever thinking, well, if we've measured garbage, you know, if we've, if what we're measuring is unreliable and unstable. I mean, this is one of the times that I often thought, you know, that psychologist who thought a lot more about measurement and principles of measurement, we needed to have integrated more of that in terms of statistical thinking. And, and I think that you all are really pressing a really important point is in the work that you've been doing.
Suzanne Thornton
Thank you so much. I yeah, I think you're you're spot on. This is this is very much in line with our thinking as well.
Dooti Roy
And we are thinking of what we can do more as illustrations, to be honest, I mean, one of the things that we do very often and I was just telling Suzy, like, two days ago about her ideas, we were like, you know, every time we have this conversation with a non believer, why why should we? Why should we do this? This is just like, you know, a few people, let's not worry, because it's adaptations. We are also taught, like, just look at the summary numbers. Yep, yep. Right? And if the summary number is like point 5%, do we care? The problem is, I think we as statisticians can do more with our simulation tools. Whenever we are looking at a problem. We are always trying to simulate and show you the pros and cons. But somehow someone hasn't done this yet for this topic. And there is also some power to
Suzanne Thornton
not to give away future research topics. But But in general, it's like this, this, this just needs to be out there.
Rosemary Pennington
Stats and Stories is a partnership between Miami University’s Departments of Statistics, and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple podcasts, or other places you can find podcasts. If you’d like to share your thoughts on the program send your email to statsandstories@miamioh.edu or check us out at statsandstories.net, and be sure to listen for future editions of Stats and Stories, where we discuss the statistics behind the stories and the stories behind the statistics.