sknox (3/26/2012)
For testing purposes (both scientific and software) pseudo-random numbers are preferable to truly random numbers*, because you want to see how the system responds to the entire range of possible inputs. A truly random number source cannot be trusted to give you a representative sample.* This is, of course, assuming that the pseudo-random number generator produces uniformly-distributed data. More on that in a bit.
That's a good point to bring up. A random distribution will create a uniform distribution across a range of data, but cannot on its own replicate any non-uniform data patterns. So if you're looking to find out if there's a normal distribution in your data (or any number of other patterns across the set), using random data may not be a good option.
This would be one of those big caveats in the "why would you need random data". The random set will allow you to test for behavior of a varity of inputs at the detail level, but won't help with test the set as a whole.
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Your lack of planning does not constitute an emergency on my part...unless you're my manager...or a director and above...or a really loud-spoken end-user..All right - what was my emergency again?