Bayesian analysis is frequently confused with conjugate Bayesian ana-
lysis. This is particularly the case in the analysis of clinical trial data. Even
though conjugate analysis is perceived to be simpler computationally (but see
below, Berger's prior), the price to be paid is high: such analysis is not robust with
respect to the prior, i.e. changing the prior may a®ect the conclusions without
bound. Furthermore, conjugate Bayesian analysis is blind with respect to the
potential con°ict between the prior and the data. Robust priors, however, have
bounded in°uence. The prior is discounted automatically when there are con°icts
between prior information and data. In other words, conjugate priors may lead
to a dogmatic analysis while robust priors promote self-criticism since prior and
sample information are not on equal footing. The original proposal of robust priors
was made by de-Finetti in the 1960's. However, the practice has not taken hold
in important areas where the Bayesian approach is making de¯nite advances such
as in clinical trials where conjugate priors are ubiquitous.