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Inclusive Education Framework: Gender identity inclusive Statistics teaching

Curriculum Community and Belonging

Going beyond the gender binary through introductory statistics teaching

Contact Details

Photo of James Gilbert

Dr James Gilbert

Department of Biological and Marine Sciences

James.Gilbert@hull.ac.uk

Case Study

Background and Activity

Upon taking over the design and delivery of statistics teaching in biology at Level 4, 3 years ago, we decided to take the opportunity to overhaul the delivery of the workshops to try and make them more inclusive.

An integral part of introductory statistics is to learn how to analyse data that are categorical, ie counts of individuals that belong to one category or another: cats or dogs, apples or oranges, etc. These are typically analysed using t-tests and chi-squared tests, some of the simplest statistical tests. To teach these tests, the curriculum as we inherited it employed what is by far the most commonly used example - that of gender, which historically has been seen as lending itself to easy counting and analysing of a supposedly binary trait among a class of students. However, representing gender as exclusively binary, and particularly asking students to declare their gender under this framework, anonymously or otherwise, risks alienating those who do not identify within that framework.

One option would have been to change the categories to something arbitrary and neutral, like apples and oranges. Instead we decided to adopt a more positively inclusive stance and retain gender as an example - continuing to analyse the distribution of gender among students in the workshop, but adding the more inclusive categories of “non-binary” and "prefer not to say".

Impact

Since adopting this measure, while working in the workshop, the students have shown no discernible immediate reaction to our inclusion of these categories, positive or negative. In all three years at least one student has self-identified as non-binary. No students have selected “prefer not to say”. The material effect of this upon teaching has been to require us to explain in the workshop how to deal with categories that have few and/or zero representatives in analyses - something with learning value to the students.

Beginning in the very first year, we saw a very positive reaction *after* the workshop, from those students identifying as non-binary. We have been approached independently by several of these students expressing thanks for taking this inclusive stance. One student even stated that this action had helped them “come out” as non-binary and has since become more confident in adopting this identification.