I kind of understand what "overfitting" means, but I need help as to how to come up with a real-world example that applies to overfitting. 
Here's a nice example of presidential election time series models from xkcd:


There have only been 56 presidential elections and 43 presidents. That is not a lot of data to learn from. When the predictor space expands to include things like having false teeth and the Scrabble point value of names, it's pretty easy for the model to go from fitting the generalizable features of the data (the signal) and to start matching the noise. When this happens, the fit on the historical data may improve, but the model will fail miserably when used to make inferences about future presidential elections.
