Chapter 17 Conditional Probability712
so
Pr
AjB
D
9
10
DPr
AjC
:
Also, since 0 is the only outcome inB\Cand 0 ...A, we have
Pr
AjB\C
D 0
So the right hand side of (17.3) is 1.8, while the left hand side is a probability which
can be at most 1—actually, it is 18/19.
17.6 Simpson’s Paradox
In 1973, a famous university was investigated for gender discrimination [5]. The
investigation was prompted by evidence that, at first glance, appeared definitive: in
1973, 44% of male applicants to the school’s graduate programs were accepted, but
only 35% of female applicants were admitted.
However, this data turned out to be completely misleading. Analysis of the in-
dividual departments, showed not only that few showed significant evidence of
bias, but also that among the few departments thatdidshow statistical irregulari-
ties, most were slantedin favor of women. This suggests that if there was any sex
discrimination, then it was against men!
Given the discrepancy in these findings, it feels like someone must be doing bad
math—intentionally or otherwise. But the numbers are not actually inconsistent.
In fact, this statistical hiccup is common enough to merit its own name:Simpson’s
Paradoxoccurs when multiple small groups of data all exhibit a similar trend, but
that trend reverses when those groups are aggregated. To explain how this is pos-
sible, let’s first clarify the problem by expressing both arguments in terms of con-
ditional probabilities. For simplicity, suppose that there are only two departments,
EE and CS. Consider the experiment where we pick a random candidate. Define
the following events:
AWWDthe candidate is admitted to his or her program of choice,
FEEWWDthe candidate is a woman applying to the EE department,
FCSWWDthe candidate is a woman applying to the CS department,
MEEWWDthe candidate is a man applying to the EE department,
MCSWWDthe candidate is a man applying to the CS department.
