  Scientific fraud is an increasingly vexing problem. Many current programs for
fraud detection focus on image manipulation, while techniques for detection
based on anomalous patterns that may be discoverable in the underlying
numerical data get much less attention, even though these techniques are often
easy to apply. We employed three such techniques in a case study in which we
considered data sets from several hundred experiments. We compared patterns in
the data sets from one research teaching specialist (RTS), to those of 9 other
members of the same laboratory and from 3 outside laboratories. Application of
two conventional statistical tests and a newly developed test for anomalous
patterns in the triplicate data commonly produced in such research to various
data sets reported by the RTS resulted in repeated rejection of the hypotheses
(often at p-levels well below 0.001) that anomalous patterns in his data may
have occurred by chance. This analysis emphasizes the importance of access to
raw data that form the bases of publications, reports and grant applications in
order to evaluate the correctness of the conclusions, as well as the utility of
methods for detecting anomalous, especially fabricated, numerical results.
