Matt Miller (#4) (3/26/2012)

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Jeff Moden (3/26/2012)

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Matt Miller (#4) (3/26/2012)

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sknox (3/26/2012)

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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.

Hmmmm... the constraints on range and domain aren't enough to satisfy this problem? Such constraints could actually form a "bell curve" (or whatever) using a CASE statement to "weight" the outcome of the constrained random generator.

That's kind of what I meant by the "on its own" comment. You can use the random data generator to pull in representative data in all allowed ranges, but you would need to play with the frequency or weight based on how far away from the mean you happen to be. Assuming you have some knowledge of your data, you can shape your test data to match, using the random set as a base.