A feminist approach to the introductory statistics course

Ayers-Nachamkin, B.
Women's Studies Quarterly

That women on the average tend to suffer from math anxiety and to perform less well in advanced mathematics classes, when they are found there at all, are repeatedly documented facts that operate as highly effective barriers to women's achievement in a variety of domains. As a math anxious individual, I avoided all math in high school and agonized through the necessary courses as a traditionally aged student in college, and again as a returning student in graduate school. It seems ironic that one of the first courses I instituted when I became a college professor at a small liberal arts college for women was an introductory statistics course. Social psychology is my discipline, however, and one of the changes I noted between the time I earned my bachelors degree in 1964 and the time I entered graduate school in 1977 was that women had become a great deal more visible in psychology, even powerful in some instances. It seemed to me that many of these women also tended to be first-rate statisticians; in fact, rather than being intimidated by numbers, these women were actually using sophisticated statistics to help write women back into psychology. I decided to do what I could to work through my own math anxiety, and, in turn, to try to teach statistics in such a way that others, regardless of their discipline, would find the subject approachable, useful, even fun from their first exposure at the college level. In the beginning, I conceived of the course as simply taking a math-anxious approach. As time has gone by, I have learned more about Feminism as a philosophy/ideology and have begun to recognize that what I had called a math-anxious approach to statistics was actually a Feminist approach. With that recognition, I have begun to apply those principles even more consciously.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education