LGBT+ resources for statisticians and data scientists

Pride art

In professional and classroom settings, we strive to communicate the intention of a respectful and productive interaction. In order to accomplish this, we all need to be mindful of our implicit biases, especially when beginning new professional relationships and establishing learning environments. The resources provided in this article are presented with this very intention.

Editor's note For further discussion of LGBT+ inclusion in statistics and data science, see this related article from our June 2019 print edition.

We have organized the following resources and tips as outlines and checklists for ease of implementation and adaptation. This resource should be viewed as an initial (and by no means exhaustive) reference and we encourage your contributions. Please use the comments section or this Google form to contribute your additional resources or tips.

In the classroom

Checklist for the first day of class

This checklist is largely adapted from Mt. Holyoke’s Supporting Trans and Non-Binary Students, a very thorough reference that we highly recommend looking through.

  • Be mindful when taking attendance. Student’s names may not be accurately reflected on your official course lists. Instead of verbally calling roll (which can inadvertently out a student as transgender), allow students to write down the name they want to be called, the name that is on their official course registration (if different), preferred pronouns, along with other information you would like to know about each student (reason for enrolling, class year, etc.). Let your students know that they can tell you if they change their name and/or pronouns over the course of the semester.
  • Set the tone with introductions. When introducing yourself to the class, consider providing your own preferred pronouns as a small gesture that welcomes students to do the same. If you ask students to introduce themselves to the class, suggest that they can provide their pronouns, but don’t require students to do so as that may put some students who are not comfortable with disclosing their identity on the spot.
  • Don’t assume that you will be able to discern a student’s pronouns or identity from their appearance. Gender identity and gender presentation are unique to each individual and may differ or change over time. Recognizing and not acting on your latent assumptions about gender presentation is a constructive habit.
  • Set classroom ground rules. The first day of class is often used to convey expectations for classroom behavior. When discussing policies on laptop usage, class participation, etc., consider also letting students know that they are expected to respect each other which includes referring to classmates with the name and pronoun that they identify with. Make it clear to students that intentional misgendering is not respectful behavior and will not be tolerated.
  • Know that you will make mistakes. We are all learning, and a part of learning is making mistakes. When you call someone by the wrong name or use an incorrect pronoun, apologize briefly and in a timely manner. Do not put the student on the spot, and do not try to justify why you made the mistake. It is not the student’s responsibility to correct you or to console you after you make a mistake. It is your responsibility to acknowledge your mistakes and put efforts into not making them again.
  • Refer to the class in an inclusive way. There are many ways to refer to your class that are inclusive of all students. For example “Welcome class/students/everyone/y’all/people/statisticians” are inclusive of non-binary students while “Welcome ladies and gentlemen” or “Welcome guys” is not.
  • Do your homework. Recognize that this type of education is a personal responsibility and that there are many resources available to learn how to support your LGBT+ students. Utilize ease of a quick internet search but be mindful of the sources that you reference.

Tips for using data in class

Since human data is an integral part of our field, it is important to provide examples that do not exclude LGBT+ students and are respectful of the study subjects and their identities.

  • Avoid using variables that reinforce the idea that gender is dichotomous or that exclude LGBT+ people. For example, consider left-handed vs. not left-handed, STEM major vs. not STEM major, etc. If you have a hard time identifying other binary variables, ask your class to identify them as an activity.
  • Avoid presenting data analyses that reinforce negative stereotypes about marginalized groups, including LGBT+ people. For example, the IPUMS Health Surveys portal allows you to create your own data sets that include sexual orientation, race/ethnicity, and many other variables (health insurance coverage, vaccinations, physical activity, etc.) that do not reinforce stereotypes.
  • Present analyses that are inclusive of LGBT+ people. For instance, an example discussing the correlation between age of husband and wife pairs excludes many LGB couples, whereas correlation of age between spouses does not.
  • Give context when using data where gender is dichotomized. Discussing the survey writing process can be helpful for students to think more critically about how their data are generated and can help trans and non-binary students feel included. For example, “The researchers in this study decided to treat gender as a dichotomous variable. How does that choice influence the questions we can answer with these data? How might this question introduce bias into the dataset?” or “Sometimes researchers choose to represent gender as a dichotomous variable. What are the statistical benefits and drawbacks of that choice? Would you make the same choice?” Keeping the conversation focused on statistical implications and making sure to differentiate between sex and gender helps normalize gender diversity.
  • Be mindful when collecting data on students for in-class exercises. Conducting an in-class survey and then using that survey data in an exercise can be very engaging for students, as long as no student is excluded. If gender data is collected, make sure that students can identify themselves in the way that best describes them, including “non-binary”, “agender”, or “other”, etc., and recognize that not every student will feel comfortable publicly disclosing this information.


Here is a brief list of some LGBT+ inclusive human datasets (organized alphabetically). We welcome feedback if you have any other recourse to share.

In the workplace

Checklist for employers and institutions

Most of the content for this list came from the LGBT+ Inclusivity in Physics and Astronomy: A Best Practices Guide, which was written primarily for universities. However, much of what was described could be used by any institution or employer including academia, industry or government.

  • Ensure a supportive climate. Employers and universities should conduct annual surveys on diversity and include issues related to LGBT+ people. The climate survey should identify issues and determine if LGBT+ needs are being met. Appropriate action should follow the climate survey.
    • Recognize that a person’s name on their transcript or CV or their name/pronouns in recommendation letters may be different from what you expect.
    • Recognize that in many places of the world, including US states, it is still legal for people to be fired for being LGBT+ so provide non-discrimination policies and legal resources available to all employees.
    • Ask questions that are relevant to the job itself and not personal questions about things that pique your interest.
  • Ensure safe incident reporting. Institutions should establish an environment whereby students and employees can safely report incidents of harassment or prejudice. This includes creating a process for reporting incidents, advertising the process, ensuring options for anonymous reporting, providing training, and releasing anonymized statistics on incidents.
  • Disseminate contact information for dual-career couples. Dual-career couples need information on resources available to them in order to make decisions regarding employment. This type of information should be inclusive of same-gender couples. Institutions should provide information on such resources via a website and should designate a contact person working outside of the hiring or admission process.
  • Use LGBT+ inclusive language. Institutions should endeavor to use gender-neutral language in policies, application materials, web pages, and other publications.
    • Use professional salutations such as “Dear Colleagues” rather than “Dear Ladies and Gentlemen”.
    • Advertise job openings and graduate school admission information in a variety of venues, including LGBT+ publications.
  • Provide an inclusive environment. Be respectful of other’s personal choices.
    • Endeavor to provide accessible gender-neutral restrooms.
    • Diversify your hiring and admissions committees.
    • Have employee resource groups (ERGs) for students.
  • Provide ally training. Employers should create and offer training for allies of the LGBT+ community as part of a comprehensive diversity program to increase advocacy and non-discrimination.
    • Seek out ally training if you are an employee and your if workplace offers it.

Checklist for supporting colleagues

Here we propose suggestions for LGBT+ allies and colleagues in the workplace.

  • Support a climate survey of employees and students. Recognize that such data is important to create and implement policies that support minority colleagues.
    • Answer climate survey questions honestly and openly.
    • Understand the diversity elements you personally bring to the workplace.
  • Attend ally training. Personal education is an ongoing process and even the most knowledgeable ally can always learn more.
    • Know where (and how) to educate yourself about diversity at your institution.
    • Respect a person’s right to out themselves to others (or respect their decision not to out themselves).
  • Become an open advocate. By being an open ally to LGBT+ colleagues you can help create a safe space for others to be themselves and
    • Take the time to actively listen when others speak.
    • Ask people their preferred name and pronouns when you meet them and consider offering your own pronouns when introducing yourself.
    • Admit when you don’t know something and/or hear an unfamiliar term.
    • Correct yourself when you make a mistake. (For example, if you use the incorrect pronoun for someone, you can briefly apologize and correct yourself and try to not make the same mistake again.)
    • Speak up when a colleague makes an improper or indecent (or otherwise disrespectful) comment or joke.
  • Support diversity initiatives. Be aware of and actively contribute to your workplace initiatives on diversity and inclusion.
    • Let minorized people speak for themselves when they are present and speak up for minority groups when they are not present or able to speak for themselves.
    • Recognize that some people have multiple minority statuses (e.g., race, gender, ethnicity) and that the confluence of these attributes helps make them the person they are. This junction of multiple minority statuses is called “intersectionality” and is something that we can all pay attention to.
    • Prevent the “tokenization” of minorized people by not assuming that any one person represents all people with a particular minority class.
  • Become culturally competent. Welcome ideas that are different from your own.
    • Seek to understand, communicate with, and interact with persons from diverse cultures.
    • Endeavor to recognize and understand the implications your own privilege may have in the workplace.

Additional resources

Here are a few additional resources for LGBT+ statisticians and data scientists and allies (organized alphabetically). This is not by any means an exhaustive list and we would love to hear from you if you have more resources to share.

About the authors

Donna LaLonde (she/her/hers) is director of strategic initiatives and outreach at the American Statistical Association.

Wendy Martinez (she/her/hers) is director of the Mathematical Statistics Research Center at the Bureau of Labor Statistics.

Jack Miller (they/them/theirs, or he/him/his) is a lecturer and statistics education specialist at the University of Michigan.

Miles Ott (he/him/his) is assistant professor of statistical and data sciences at Smith College.

Suzanne Thornton (she/her/hers) is a PhD candidate in the Department of Statistics at Rutgers University.