  Software Engineering (SE) experiments are traditionally analyzed with
statistical tests (e.g., t-tests, ANOVAs, etc.) that assume equally spread data
across treatments (i.e., the homogeneity of variances assumption). Differences
across treatments' variances in SE are not seen as an opportunity to gain
insights on technology performance, but instead, as a hindrance to analyze the
data. We have studied the role of variance in mature experimental disciplines
such as medicine. We illustrate the extent to which variance may inform on
technology performance by means of simulation. We analyze a real-life
industrial experiment on Test-Driven Development (TDD) where variance may
impact technology desirability. Evaluating the performance of technologies just
based on means-as traditionally done in SE-may be misleading. Technologies that
make developers resemble more to each other (i.e., technologies with smaller
variances) may be more suitable if the aim is minimizing the risk of adopting
them in real practice.
