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Sexual and gender minorities face considerable inequities in society, including in science. In biology, course content provides opportunities to challenge harmful preconceptions about what is “natural” while avoiding the notion that anything found in nature is inherently good (the appeal-to-nature fallacy). We provide six principles for instructors to teach sex- and gender-related topics in postsecondary biology in a more inclusive and accurate manner: highlighting biological diversity early, presenting the social and historical context of science, using inclusive language, teaching the iterative process of science, presenting students with a diversity of role models, and developing a classroom culture of respect and inclusion. To illustrate these six principles, we review the many definitions of sex and demonstrate applying the principles to three example topics: sexual reproduction, sex determination or differentiation, and sexual selection. These principles provide a tangible starting place to create more scientifically accurate, engaging, and inclusive classrooms.

Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.