Non-citation of basic knowledge We all know how important proper attribution of ideas is. At the same time, certain things have become basic enough that citing the paper where they were first discussed is overkill: to give an extreme example, if you need to do some differentiation in your math/physics paper, you don't need to go and cite Newton and Leibniz. Now, on occasion students ask me how one can determine if a piece of knowledge is common enough that they can forgo a citation. The rule of thumb I give them is:


  If it is something that is explained in a standard first-year undergrad textbook, then anybody who is going to read your papers knows about it and you don't need to provide a citation.


[Here I want to emphasize that I give this to them as a rule of thumb, and I always tell them to ignore it and provide the relevant citation if they think it is necessary to do so in a specific case]

Are there better or alternative ways of drawing the line?
I find Latour and Woolgar's spectrum of "facticity" a useful tool for thinking about these questions (a nice summary can be found at this link).  It breaks scientific statements into five rough categories by level of certainty in the assertion:


Speculations - don't have to be backed by anything
Descriptions - not established, so need to be directly backed by evidence
Tentatively established - need to be backed by citations
Well accepted - should be stated, but don't need evidence or citations
Tacit - should not even be stated


Where exactly a fact lies on this spectrum depends on the community and state in discussion.  In general, the broader the audience, the less well accepted facts should be assumed to be.  I think the notion "Should everybody reading this have been taught in a class?" is a good one, though undergraduate is not necessarily the stopping point.  For a machine learning audience, for example, you should assume everybody has had graduate level machine learning courses, while for a biology audience you should not assume they have even had undergraduate computer science.