The International Biometrics Society Western North American Region meeting recently featured an invited session, titled “Considerations and Best Practices for Using Race, Ethnicity, Ancestry in Different Areas of Statistics and Data Science Research,” organized and chaired by Maricela Cruz and Audrey Hendricks.

The Math Alliance is a national mentoring community focusing on increasing traditionally excluded American minorities in the quantitative sciences professions.

The Justice, Equity, Diversity, and Inclusion Outreach Group is sponsoring or co-sponsoring more than a dozen activities at JSM showcasing work to promote JEDI in the practice and profession of statistics.

The Michael Tsiang Fund for Belonging in Statistics is an initiative to create a safe and supportive community around the many groups that have been historically underrepresented in statistics.

It is commonly believed that lesbian, gay, bisexual, transgender, queer, intersex, and asexual populations are “hard to reach” for research participation. Not so, say the three expert presenters for the ASA LGBTQ+ Advocacy Committee’s inaugural webinar, “Sociocultural and Analytical Considerations for Engaging LGBTQIA+ Communities.”

For students with marginalized identities, a conference may be the first place they encounter other statisticians with similar identities, which could contribute to a sense of belonging in the field. However, equitable accessibility to conferences is not always available for these students.

The Collaborative Undergraduate Biostatistics Experience (CUBE) is gearing up for its second year at Virginia Tech this summer. CUBE exposes undergraduate STEM students, specifically those who are part of an underrepresented/minority population, to the data science and collaborative biostatistics fields.

Lydia Gibson looks back at 2022 and discusses all the ASA JEDI Student and Young Professionals Committee accomplished.

Traditional approaches to secondary data analysis do not involve the people most affected by the analysis, which can create distance between statisticians or data scientists and the communities behind it. By connecting secondary data analysis with community-based research, we can decrease this distance.

Data visualization and exploratory data analysis have been recognized in recent decades as important parts of statistics, but visualization excludes people with visual impairments, an increasingly important issue as the population gets older. How can statisticians and analysts provide some of the benefits of the dataviz revolution?