Stereotypical Understanding

11 Jul

The paucity of women in Computer Science, Math and Engineering in the US is justly widely lamented. Sometimes, the imbalance is attributed to gender stereotypes. But only a small fraction of men study these fields. And in absolute terms, the proportion of women in these fields is not a great deal lower than the proportion of men. So in some ways, the assertion that these fields are stereotypically male is in itself a misunderstanding.

For greater clarity, a contrived example: Say that the population is split between two similar sized groups, A and B. Say only 1% of Group A members study X, while the proportion of Group B members studying X is 1.5%. This means that 60% of those to study X belong to Group B. Or in more dramatic terms: activity X is stereotypically Group B. However, 98.5% of Group B doesn’t study X. And that number is not a whole lot different from 99%, the percentage of Group A that doesn’t study X.

When people say activity X is stereotypically Group B, many interpret it as ‘activity X is quite popular among X.’ (That is one big stereotype about stereotypes.) That clearly isn’t so. In fact, the difference between the preferences for studying X between Group A and B — as inferred from choices (assuming same choices, utility) — is likely pretty small.

Obliviousness to the point is quite common. For instance, it is behind arguments linking terrorism to Muslims. And Muslims typically respond with a version of the argument laid out above—they note that an overwhelming majority of Muslims are peaceful.

One straightforward conclusion from this exercise is that we may be able to make headway in tackling disciplinary stereotypes by elucidating the point in terms of the difference between p(X|Group A) and p(X| Group B) rather than in terms of p(Group A | X).