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No, 'uniform' refers to the distribution, which need not be uniform, e.g.

https://numpy.org/doc/stable/reference/random/generated/nump...

Or in god's own words (TAOCP section 3.4):

Applications of random numbers often call for other kinds of distributions, however; for example, if we want to make a random choice from among k alternatives, we want a random integer between 1 and k. If some simulation process calls for a random waiting time between occurrences of independent events, a random number with the exponential distribution is desired. Sometimes we don't even want random numbers — we want a random permutation (a random arrangement of n objects) or a random combination (a random choice of k objects from a collection of n).

In principle, any of these other random quantities can be obtained from the uniform deviates U0, U1, U2, ...; people have devised a number of important "random tricks" for the efficient transformation of uniform deviates. A study of these techniques also gives us insight into the proper use of random numbers in any Monte Carlo application.




"All distributions are uniform" is one of the two cardinal crimes of a school level of statistics understanding, the other being "all probabilities are independent".




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