What is the probability that the product of $20$ random numbers between $1$ and $2$ is greater than $10000$? Twenty random real numbers $a_1,a_2,\dots,a_{20}$ are chosen such that $1\le a_i \le 2$. What is the probability that their product is greater than $10000$?
(By random, I mean each real number in the interval $[1,2]$ has an equal chance of being chosen. All twenty numbers are chosen independently of each other.)
 A: This is more thinking aloud than real answer.
A paper called Product of n independent uniform random variables by Carl P. Dettmann, Orestis Georgiou seems to be relevant. Its central result is following theorem:

In our case $a=1$, $b=2$, $n=20$.
After a lot of cumbersome but doable calculations it is possible to obtain the probability from the question using PDF from above Theorem 1.
A: Here´s an aproximation with a normal distribution.
$${Let\quad  Y_i=\ln( X_i)    \quad   where    \quad X_i \thicksim U(1,2) \quad i=1,2, \ldots,20 }  \qquad {then \quad f_{Y_i}(y_i)=e^{y_i}\quad 0\leq{y_i}\leq \ln(2)}                  $$
$$And \space then\quad {\mu =\int_0^{\ln(2)} ye^y dy =0.386294}\qquad{E[y^2]=\int_0^{\ln(2)} y^2 e^y dy =0.188317 }\qquad{\sigma=0.197722}$$
$$Now,\space from\space probability\space we\space know \space that:\qquad {\lim_{n\to \infty}  Pr(Z_n= \dfrac{S_n-n\mu}{\sigma \sqrt n}\leq z )=\Phi (z)} $$
$$ You´re\space looking\space for \qquad {\prod_{i=1}^{n}  X_i \geq C  }\qquad$$$${This\space means\qquad S_n=\sum_{i=1}^{n}  \ln(X_i)=\ln(\prod_{i=1}^{n} X_i) \geq \ln(C) }\qquad $$$${Applying\space the \space C.L.T\space theorem:}$$$$\qquad \qquad {P(S_n \geq \ln(C))=P(S_n -n \mu \geq \ln(C) - n \mu)=P(\dfrac{S_n-n\mu}{\sigma \sqrt n}\geq \dfrac{\ln(C)-n\mu}{\sigma \sqrt n})={1-P(\dfrac{S_n-n\mu}{\sigma \sqrt n}\leq \dfrac{\ln(C)-n\mu}{\sigma \sqrt n})}\approx 1-\Phi( \dfrac{\ln(C)-n\mu}{\sigma \sqrt n})}$$
In your case n=20 (akward, since it´s not big enough to normal-approximate) and C= 10,000.
So the probability (approximately ) is  4.6596% (Montecarlo simulations says 4.51%).
A: Using the CLT on the logs as suggest by @kennytm might give a
satisfactory approximation. (Of course the CLT does not apply
directly to products.)
As long as we're discussing approximations, a simulation gave approximately 0.045 as the answer. 
Repeated it several times to check computational stability.
One run is shown below.
(Each row of the matrix has 20 observations from UNIF(1, 2);
the penultimate statement takes products of rows; the
last statement estimates the desired probability baed on a
million performances of the experiment.)
 m = 10^6; n = 20
 MAT = matrix(runif(m*n, 1, 2), nrow=m)
 y = apply(MAT, 1, prod)
 mean(y > 10000)
 ## 0.045291

Addendum: A histogram of the sums of logs of 20 obs
is nicely fit by a normal density curve. This seems to
lend credibility to the approach of using the CLT on logs.
