OK, I just carried out a chi-squared test on the hypothesis that there was a bias towards any numbers (i.e. they are not uniformly distributed). It is set up as follows:

Hypotheses:

Null hypothesis: The distribution of numbers is uniform.

Alternative hypothesis: The distribution of numbers is not uniform.

Level of test:

95% is the standard level.

Degrees of freedom, v:

There are nine degrees of freedom as there are ten classes and one restriction (one of the values can be worked out if you know nine of them and the total).

Rejection criterion:

If the calculated chi-squared calue is greater than the chi-squared value for 9 degrees of freedom at 95% then we reject the null hypothesis in favour of the alternative.

i.e. if the calculated chi-squared value is less than 16.919 (looked up on a table i have) then we accept the null hypothesis that the numbers are uniformly and randomly distributed and that there is no bias towards any numbers.

Chi-squared calculation:

chi-squared = sum of [{|observed value - corresponding expected value|^2}/expected value]

= [{|34 -30|^2}/30] + [{|22-30|^2}/30] etc

=11.933

Therefore we accept the null hypothesis that there is no bias in the distribution of the numbers.

In statistics anything stated with much less that 95% certainty is disregarded.

Also, I know very little of the Mersenne Twister method but it is very widely used for it's speedy production of very near random numbers. All random number generators on computers create lists of pseudorandom numbers. No method is perfect on a computer, they're just very close.

Oh, I only typed out the method so people could see what was going on...

Thanks for your time
superdumprob

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"If we knew what it was we were doing, it would not be called research, would it?" - Albert Einstein