Are these tests mathematically motivated?  Did you know the answer beforehand? Did you verify it somehow?

I remember when I first started writing tests I would just use the code, see the result, and copy that result into a test.  My guess is that that is what you did here.  Usually this doesn't add value but does increase test time (something we'd like to minimize).  A good test should be easy to compute and easy to verify.  

For example I think that the following tests are easily verifiable.

```
X = Normal('x', 0, 1)
Y = Normal('y', 0, 1)
assert variance(5*X) == 5**2 * variance(X)  # variance scales quadratically
assert variance(X+Y) == variance(X) + variance(Y)  # variance is linear for independent variables
```

The tests you have that are like `variance(expression) == cmoment(expression, 2)` seem really good to me.  It's clear what they mean and I definitely want those statements to be true.  When I look at `correlation(X, X*X) == 7*sqrt(11505)/767` I'm not actually sure that that should be true without breaking out my calculator and doing a bunch of work.  The only value this gives to me is that I know that the expression doesn't produce an error.

I suggest that you keep the tests that check against the known relationships (like `variance` and `cmoment(..., 2)`) and remove any test that you can't verify in a reasonable amount of time.  Then we should probably add tests for a few of the lesser known degrees because they won't be covered by existing tests on expectation, variance, skewness, etc... 

Are there other known relations on moments that we can check?

* What should happen when `n==0`?  Does the code satisfy this?  I actually haven't though about the answer here but I suspect there is a trivial result.
* My understanding is that all central moments of any normal distribution (not just mean-0 and std-1) above `n==2` are `0`, is this satisfied for a few values?
* By looking at [wikipedia](http://en.wikipedia.org/wiki/Exponential_distribution#Mean.2C_variance.2C_moments_and_median) I see that there is a known form for the moments of an exponential distribution.  Perhaps you could check a couple of these?