Jo Hardin (pronouns: she/her/hers) is a professor of mathematics and statistics at Pomona College. She completed her PhD at the University of California, Davis and started research in statistical analysis of high-throughput data through her postdoc at the Fred Hutchinson Cancer Research Center.

Jack Miller (pronouns: they/them/their or he/him/his) is a statistics education specialist who joined the faculty at the University of Michigan in the fall of 2013. Prior to the University of Michigan, Miller was at The Ohio State University and on the faculty of the Department of Mathematics and Computer Science at Drury University. Miller is interested in both the teaching of statistics and training future statistics teachers.

In June 2016, Jo Hardin, a cisgender (an individual whose gender matches the sex that they were assigned at birth) woman, found herself talking about randomization of study participants with the goal of balancing the proportions of men and women across treatment groups. In particular, it was important that the two study groups had, on average, the same center of gravity. (The example comes from Chance and Rossman, Investigating Statistical Concepts, Applications, and Methods, Investigation 3.4 – Have a nice trip.)

Jo had used the example many times and it had never crossed her mind that randomizing gender may be a sensitive topic for some students (indeed, the example and corresponding applet are fantastic ways to teach about randomizing to avoid issues of confounding!). But in that class of students, there were two transgender men (individuals who identify as male but were assigned female at birth) who caused her to stop and think. Jo could tell by the look on their faces that the discussion of the binary didn’t quite sit right. And why should it? Those students had both spent many years struggling with the world at large trying to fit them into a box in which they do not belong and they had both (presumably) come to a place where they feel more comfortable—which, in their cases, happened to be in another box. Theirs is just one situation. For some transfolks, there is no box that is correct—gender is not binary!

Meanwhile, on October 21, 2018, The New York Times reported that the Department of Health and Human Services is “spearheading an effort to establish a legal definition of sex under Title IX.” The proposed change would require that “key government agencies needed to adopt an explicit and uniform definition of gender as determined ‘on a biological basis that is clear, grounded in science, objective and administrable.’ The agency’s proposed definition would define sex as either male or female, unchangeable, and determined by the genitals that a person is born with, according to a draft reviewed by The Times. Any dispute about one’s sex would have to be clarified using genetic testing.”

The same week that this memo was uncovered by The Times, the Department of Justice filed a brief with the Supreme Court to support a narrow definition of “sex” that does not include transgender people. Many individuals and organizations from the Bureau of Labor Statistics to companies like Amazon, Apple, and Google are gravely concerned about this move by the administration, and many transgender persons now live in fear of being “erased” and discriminated against because they “no longer exist” with this definition.

Why should statisticians care about this issue?

We as statisticians know that (a) randomization doesn’t care about the gender binary and (b) statistics is a field of “on average.” The randomization process used to balance confounding variables is agnostic to the type of variable. One need not even adjudicate whether gender is binary before using randomization to balance treatment groups. The statistical nuances, however, are lost on students who hear Jo’s lecture and immediately feel they are made invisible by the conversation. Invisibility is less divisive than being erased, but none of us want our students to feel invisible in our classroom. So, as teachers, what do we do?

The in-class experience led to a thought-provoking discussion between Jo and Jack, a nonbinary transmasculine statistics educator who has thought long and hard about gender identity issues in the classroom on both a personal and professional level. Jack’s words of wisdom are woven in to the remainder of this article as advice for all of us in both teaching important aspects of randomized studies and relating to our students as people.

When first using the “binary” variable of gender in the classroom, what can teachers do to make the conversation more inclusive? First, mention that (in virtually all disciplines) most study participants are cisgender men and women (because there is not much research done on trans people). Also, open the discussion to ask about how the variability in the groups (e.g., with respect to center of gravity) might change if there were non-cisgender people in the study.

As it becomes safer for trans individuals to be open with their identity, the students in your class are more and more likely to have trans friends or family members. Perhaps the students (cis- and trans-) would be willing to contribute to the rich discussion on gender identity as it relates to scientific research. If not, you could mention that some transgender people choose to medically transition and that might impact center of gravity. Note: You (the instructor) may not be able to tell (visually) if you have any trans students in your class. You may have (or have had) at least one trans student who did not disclose their gender to you.

Right now, as educators, we are in an odd place with examples and actual experiments/studies. There are not enough studies that include trans, nonbinary, genderqueer, or folks of other identities for us to include in our classes, but we don’t want to be exclusive of our students who are not cisgender. Fortunately, the examples we choose to use in the classroom can be inclusive.

The conversation around gender identity reminds us of statistics textbooks from the mid-1990s that used only Black and White for race. At that time, it was difficult to navigate the examples in a room full of students of all races. Fortunately, our textbooks have changed over time with respect to race, which gives us hope that the classroom conversation around gender will expand to include transfolks or at least specify that (presumably) studies include cismen and ciswomen.

To date, (almost) all textbooks refer to gender as a binary when teaching proportions. This is incorrect and can negatively impact students who are not part of the gender binary. At the same time, we often feel restricted to actual research that has been done only when talking about the studies we use while teaching. We can include other examples we bring in ourselves (e.g., left-handed or not? Proportion of golden retrievers or not?)

We are not advocating that statistics instructors overhaul their entire courses. We only suggest that, as teachers, we should be aware of the perspectives our students bring to the table with respect to their identities. Bringing up issues such as the gender binary within research studies will go a long way toward making the statistics material accessible to all students. As we were writing this article, we came across the Twitter conversation that we present here as an indication that others are concerned with the same issues we are.

Perhaps the little things we do with our students will help us teach better and think more carefully about research—when is it okay to block by gender? Does blocking by gender still have meaning as the world changes to include gender on a spectrum? We believe it does because most of the world can be reduced to a binary in terms of gender. Thus, centers of gravity will, for the most part, still behave like we would expect in a binary world. We do not think the binary should be thrown out; we just want to point out the sensitivity we need in terms of working with our students, not all of whom will identify with the binary.

Originally, we hoped to share our experience with you to open the discussion of inclusivity in the classroom. Now that the federal government is taking steps to “erase” transgender persons, it is even more urgent for academics and practitioners alike to think about gender issues and how we, as statisticians, define and categorize variables. We welcome you to share your ideas with us, so we can help all students and coworkers. If you have any ideas and/or want to discuss this further, feel free to contact either or both of us: Jo Hardin or Jack Miller.