The last few weeks, I got my hands dirty building a parser that can analyze a user's itunes.xml file, the file that contains all a user's songs, albums, genres and number of song plays, song lengths and anything else you can think of in regards to a music library. I collected these xml files from some friends and did some quick analyses to see what new and interesting ways there are to look at this data.
One element in the xml is song length, so I thought it would be interesting to compute the probability distribution of song length, which is shown in the following figure:

This plot was calculated from over 70,000 songs from 12 libraries. The median of this plot is 231 seconds ans the mean is at 242 seconds. Its interesting that this data might reflect something very basic about what kind of music human beings like:
songs that are nearly exactly 4 minutes long. As you move away from the 4 minute mark, the probability drops in similar amounts (the plot is symmetric (ish).
Each of the libraries themselves displayed similar distributions, depending on what genre's of music they listened to. In the next figure, I looked at the song length probabilities of different genres of music (as labeled in the itunes file, not always an appropriate label, but a good approximation).

The blue (shortest) are punk rock songs, the red are hip hop and the yellow are techno. Its interested that the hip hop songs, which generally speaking have the largest audience (the distribution for songs labelled "pop" is also very similar) centers itself directly on the 4 minute mark. The techno and punk rock songs deviate slightly (in opposite directions) from this mark, and the smaller, more niche following that these genres attract seems like logical result. Thats all for now, I have other interesting observations that I'll share in the near future. Please feel free to suggest your own things to take a look at.