I have read in literature that in some cases the training set is not representative for a real-world dataset. However, I cannot seem to find a proper term describing this phenomenon; what is the proper term to address this problem? Edit:So far I have settled for the term domain adaptation, shortly described as a field in machine learning which aims to learn from a certain data distribution in order to predict data coming from a different (but related) target distribution. 
Discrepancy between training set and real-world data set: domain adaptation?
machine learning;predictive modeling;dataset;domain adaptation