Ewx's recently made a poll on "number of sexual partners". The graph appeared rather like a poisson distribution[1] but it's hard to actually deduce anything for sure as (a) rounding to the nearest 3 obscures the first part of the hump and (b) most things look vaguely like a poisson anyway.
Statistics is hard. I know a little about the most common distributions, but not really what happens when you start combining them.
The sum of two poisson random variables (even with different means) is another poisson random variable. So I hypothesised that the the number of sexual partners a person has would be a poisson distribution -- if you assume each year they have a poisson-distributed number of partners (eg. mean 1/20 for someone in a stable relationship, 100 for someone very promiscuous), and those means are fixed in advance, it seems the sum to any point in their life will be a poisson.
However, Question 1 is, "What if those means aren't fixed in advance?" Is there a convenient distribution on some appropriate assumptions, or is it just hard?
Question 2 is, supposing each person were a poisson distribution, with means were distributed in a certain way (say, normal distribution), does that give any coherent distribution when you count the number of different people with a number of partners?
Question 3 is, is there any data on this, from Kinsey or anywhere? A cursory search didn't show it. What should ewx's poll show? A hump at 0 and 1? A poisson? Might people's means cluster, if you classed people into different categories, might you see several overlayed poisson curves?
[1]
Poisson distribution is what lots of discrete things end up as. If you have a very large binomial distribution, eg. toss 1000 coins with 1/1000 chance of coming up heads, it strarts to look like a poisson. Although what it really measures is when the "time until the next event" is random, ie. exponentially decays.