I’ve been spending the week at the Infectious Disease Modeling Summer School here at UW. It’s very interesting, and good for me to learn more about how people in my new field think (especially people in my new field, *outside* of my little institute…)

I’ve discovered a pet peeve during this week of presentations, though. I’ve seen a lot of numerical examples where the numbers work out perfectly… a little too perfectly. If you split 1000 people into an experimental and control group by choosing a random subset of 500, fine. But if you look within that group to see how many have a trait that occurs independently with probability 0.2, you do not often find exactly 100 in group A and 100 in group B. I think a little more complexity in the numbers makes the example *easier* to understand.

I’m sure that you, my loyal reader, can generate random numbers from a multitude of distributions, if you wanted to spend the time. But if you’re busy, busy, busy, then you can have wolfram alpha do all the work. It actually comes through for that one: “sample Binomial(500, .2)“.

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p.s. when I just did sample twice from Bi(500, .2), I got 101 and 101. Maybe examples should use numbers that are more random than random!