A flowchart for concentration bounds When I teach tail bounds, I use the usual progression: 


If your r.v is positive, you can apply Markov's inequality
If you have independence and also bounded variance, you can apply Chebyshev's inequality
If each independent r.v also has all moments bounded, then you can use a Chernoff bound. 


After this things get a little less clean. For example


If your variables have zero mean, then a Bernstein inequality is more convenient
If all you know is that the combining function is Lipschitz, then there's a generalized McDiarmid-style inequality
if you have weak dependence then there are Siegel-style bounds, (and if you have negative dependence, then Jansson's inequality might be your friend)


Is there a reference anywhere to a convenient flowchart or decision tree describing how to choose the "right" tail bound, (or even when you have to dive into a sea of Talagrand) ? 

I'm asking partly so that I have a reference, partly so that I can point it to my students, and partly because if I'm sufficiently annoyed and there isn't one, I might try to make one myself.
Fan Chung and Linyuan Lu. 
Concentration inequalities and martingale inequalities: a survey
available at
http://projecteuclid.org/euclid.im/1175266369 or at Fan Chung Graham's web page.