A large body of research in the cognitive sciences relies on
examining statistical differences. While the approach of
examining differences can aid in explaining behavior, it does
not necessarily mean that these differences have predictive
power. Yet, understanding behavior both involves explaining
and predicting behavior. As a point in case, the current study
used a naturalistic email dataset to examine statistical
differences and predictive power in fraudulent activities.
Differences between 1st and 3rd person pronoun use in liars and
people telling the truth are widely reported in the literature. The
current study aimed to test for the effect of fraudulent events
on pronoun use in emails using the Enron corpus and
additionally applied a machine learning approach to estimate
whether pronoun use predicts fraud. While the ratio between
1st and 3rd person pronoun use was related to fraud, this
construct did not have predictive power. The current study
highlights an important conclusion for the cognitive sciences:
The importance of not only testing for differences, but of also
applying predictive models. In this way it can be determined
whether effects of a construct on an outcome can also predict
the outcome.