I've found the MIT open courseware material a godsend. MIT offers a course, Mathematics for Computer Science (2002) with a significant section on probability theory, including the bounding techniques we've been studying. If you want a good crash course in stats I highly recommend reading the notes on lectures 10-14. The notes are clear and the examples fascinating. I'll share one of my favorites. Professor Chernoff did an investigation off the Mass. lottery, described in the notes for lectures 13-14:
There is a lottery game called Pick 4. In this game, each player picks 4 digits, defining a number in the range 0 to 9999. A winning number is drawn each week. The players who picked the winning number win some cash. A million people play the lottery, so the expected number of winners each week is 100... In this case, a fraction of all money taken in by the lottery was divided up equally among the winners. A bad strategy would be to pick a popular number. Then, even if you pick the winning number, you must share the cash with many other players. A better strategy is to pick a lot of unpopular numbers. You are just as likely to win with an unpopular number, but will not have to share with anyone. Chernoff found that peoples’ picks were so highly correlated that he could actually turn a 7% profit by picking unpopular numbers!Most of the state-of-the-art retrieval algorithms are based statistics and the probability of a word occurrences in a document w.r.t a collection of documents. So, even if you aren't taking a class in algorithms, it's useful background to study for search.
Thank you MIT!