  The amount of useful information available on the Web has been growing at a
dramatic pace in recent years and people rely more and more on the Web to
fulfill their information needs. In this paper, we study truthfulness of Deep
Web data in two domains where we believed data are fairly clean and data
quality is important to people's lives: {\em Stock} and {\em Flight}. To our
surprise, we observed a large amount of inconsistency on data from different
sources and also some sources with quite low accuracy. We further applied on
these two data sets state-of-the-art {\em data fusion} methods that aim at
resolving conflicts and finding the truth, analyzed their strengths and
limitations, and suggested promising research directions. We wish our study can
increase awareness of the seriousness of conflicting data on the Web and in
turn inspire more research in our community to tackle this problem.
