Reviewing some basic probability and statistics, I rediscovered the geometric distribution, which tells you the likelihood of winning a game after some number of attempts. With it, we can figure out the number of times you should expect to play the game until the outcome is “success” – or, equivalently, the number of plays you’d need in order to expect one success among them.

Why is this important? They say the odds of winning the current record-setting powerball lottery are 1 in 176 million. I want to be rich. How many tickets do I need to buy to expect a winning one?

The intuition is straightforward. For instance, if there is a p=25% chance of success on any one trial, the expected ratio of success to failures is 1/4, or one out of every four. Which means that any four trials should yield one success on average. Mathematically, if we let X be the number of trials required until a success occurs, then E[X] = 1/p.

So the odds are in my favor after buying 176 million randomly generated tickets. Who wants to loan me $176,000,000?

The proof is neat.

If p is the probability of success, and q = (1-p) is the probability of failure, then the probability of succeeding on the i’th trial after (i-1) failures is:

Pr(X=i) = q^(i-1)p


Lines three to four depend on some magic with infinite series. If you have a converging series where x < 1:

We use this last equation in the derivation above. So what else can we do with this? Suppose we want to know how many rolls of a die are needed to see all six sides come up at least once (given a six-sided, fair die, where each role is an independent event). On the first roll, the probability of getting a side we haven't seen is 1. The next role then has a 5/6 probability of yielding a unique side; and then, after the next unique number comes up, the probability becomes 4/6 ... and so on. So, 1 + 1/(5/6) + 1/(4/6) + ... = 14.7 Of course, I distrust math: I need evidence. And so Python to the rescue:


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