**November 14 at 11:00 a.m.**

Trivial Mathematics but Deep Statistics:
Simpson's Paradox and Its Impact on Your Life

Few paradoxes have impacted everyday life
more than Simpson's Paradox has. Yet paradoxically, Simpson's
paradox is not even a paradox in the mathematical sense. Simple
arithmetic can easily show that it is possible for a surgeon to
have the highest overall success rate, and yet have the lowest
success rates for each type of surgeries he performed. The fact
that you may feel this phenomenon counterintuitive is precisely
the reason that the Simpson's paradox has led to many erroneous
conclusions and decisions that affect people's life, particularly
those from social and medical studies, where comparisons using
aggregated data are routinely performed. This talk demonstrates
the danger of Simpson's paradox via a number of real-life examples,
from the famous Berkeley sex bias case to measuring disparity
in mental health service based on the recently released National
Latino and Asian American Study (NLAAS), and from batting averages
and to a recent debate on unemployment rates (Wall Street Journal,
December 2, 2009). No statistical background is required to understand
this talk, but only some common sense and a desire to think deeply
beyond formulas.

(This is also G-rated talk because it is a "gadgeted"
seminar. Never heard of it? Well, this is your chance …)

**November 15 at 11:00 a.m.**

*Who is crazier: Bayes or Fisher? *(slides)

Objective statistical inference has been
an object of desire as early as inference itself. Some consider
it an illusion; others counter that while mirages may make distant
goals appear near, they ultimately reflect reality. Most approaches
share a common oddity: in order to obtain "objective"
inference, one seems have to do something a bit crazy, at least
to those who take probability theory seriously. Objective Bayesians
advocate the use of improper prior distributions that have no
probabilistic reality, and Fisher's fiducial inference apparently
violates the most basic probabilistic laws. But while one illegality
(objective Bayes) gains ever greater popularity, fiducial inference
still languishes under an old nickname "Fisher's biggest
blunder". Does this mean Fisher was crazier than Bayes, or
is madness a mask for innovation? If you cannot infer objectively
the answer to this non-objective question, this talk will provide
a subjective answer from a missing-data perspective. (This is
joint work with Keli Liu.)

Xiao-Li
Meng is an award-winning statistician, and the Whipple V. N. Jones
Professor of Statistics at Harvard University. He received the COPSS
Presidents' Award in 2001. Since 2004 Meng has been Chair of Harvard's
Department of Statistics.Meng received his B.Sc. from Fudan University
in 1982 and his Ph.D. in statistics from Harvard University in 1990.
He was elected a fellow of the Institute of Mathematical Statistics
in 1997 and of the American Statistical Association in 2004.

The Distinguished Lecture Series in Statistical Science series was
established in 2000 and takes place annually. It consists of two lectures
by a prominent statistical scientist. The first lecture is intended
for a broad mathematical sciences audience. The series occasionally
takes place at a member university and is tied to any current thematic
program related to statistical science; in the absence of such a program
the speaker is chosen independently of current activity at the Institute.
A nominating committee of representatives from the member universities
solicits nominations from the Canadian statistical community and makes
a recommendation to the Fields Scientific Advisory Panel, which is
responsible for the selection of speakers.

Distinguished
Lecture Series in Statistical Science Index