Recently I was trying to combine p-values (e.g. by Fisher's method), but during that investigation I got led astray. I started looking at the distribution of the geometric mean of sets of independent Uniform(0,1) variables. I noticed that as the set size increased, the mean of the geometric mean’s distribution appeared to converge to a number. It appeared to converge to the inverse of Euler's number, 1/exp(1) = 0.3678794.
For set size n, I created 5000 n-vectors containing independent Uniform(0,1) draws. I calculated the geometric mean of each of the 5000 n-vectors. Then I calculated the sample mean of those 5000 geometric means. I did this for a range of set sizes from 1 to 1,000,000. Here are the results for the smallest and largest set sizes.
> head(dfRes)
iGroupSize mean
1 1 0.4856412
2 2 0.4599790
3 3 0.4188242
4 4 0.4110794
5 6 0.3960357
6 10 0.3839790
> tail(dfRes)
iGroupSize mean
26 100000 0.3678654
27 158489 0.3678691
28 251189 0.3679015
29 398107 0.3679053
30 630957 0.3678569
31 1000000 0.3678630
As you can see, for n=1 the mean geometric mean is close to 0.5 as expected, and when n is large (in fact for n>100) it was very close to 1/exp(1).
I think this connection between Uniform(0,1) and exp(1) if true is amazing. I can’t see the connection. Is this result well known? Can anyone prove this result?
Thanking you in advance.
Desmond