  Many data mining approaches aim at modelling and predicting human behaviour.
An important quantity of interest is the quality of model-based predictions,
e.g. for finding a competition winner with best prediction performance.
  In real life, human beings meet their decisions with considerable
uncertainty. Its assessment and resulting implications for statistically
evident evaluation of predictive models are in the main focus of this
contribution. We identify relevant sources of uncertainty as well as the
limited ability of its accurate measurement, propose an uncertainty-aware
methodology for more evident evaluations of data mining approaches, and discuss
its implications for existing quality assessment strategies. Specifically, our
approach switches from common point-paradigm to more appropriate
distribution-paradigm.
  This is exemplified in the context of recommender systems and their
established metrics of prediction quality. The discussion is substantiated by
comprehensive experiments with real users, large-scale simulations, and
discussion of prior evaluation campaigns (i.a. Netflix Prize) in the light of
human uncertainty aspects.
