Discrepancy between training set and real-world data set: domain adaptation? 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.
You may be looking for sampling bias. Also the other case (where training set does in fact represent the real world data-set well) is generally known as representative sample.

Hope this helps.