<p>There are NO guarantees about the quality of the pseudo random generator in standard Fortran. If you care about some particular quality of implementation for cryptography or science sensitive to random numbers (Monte-Carlo), you should use some library which you have control about.</p>
<p>You can study the manual of your compiler to find out what it says about the random number generator, but every compiler can implement a completely different algorithm to generate random numbers.</p>
<p>Numerical Recipes is actually not well received by some people in the numerical mathematics community <a href="http://www.uwyo.edu/buerkle/misc/wnotnr.html" rel="nofollow noreferrer">http://www.uwyo.edu/buerkle/misc/wnotnr.html</a></p>
<p>This site is not for software recommendation, but this article (link given by roygvib in a comment): <a href="https://arxiv.org/abs/1005.4117" rel="nofollow noreferrer">https://arxiv.org/abs/1005.4117</a> is a good review with examples of bad and good algorithms, methods how to test them, how to generate arbitrary number distributions and examples of calls of two example libraries in C (one of them can be called from Fortran as well).</p>
<p>Personally I use this <a href="https://bitbucket.org/LadaF/elmm/src/master/src/rng_par_zig.f90" rel="nofollow noreferrer">https://bitbucket.org/LadaF/elmm/src/master/src/rng_par_zig.f90</a> parallel PRNG, but I didn't test the quality, I personally just need speed. But this is not a software recommendation site.</p>
Can Random Number Generator of Fortran 90 be trusted for Monte Carlo Integration?
<p>I have written a short monte carlo integration algorithm to calculate an integral in Fortran 90. I once compared the result obtained by solving the integral with respect to some parameter using the intrinsic random number generator with the random number generator method ran1 presented in Numerical Recipes for Fortran90 Volume 2. </p>

<p>Running the same algorithm twice, once calling the intrinsic random_seed(), then always call random_number() and once calling the ran1() method provided in the Numerical Recipe book I obtain as result in principal the same shape but the intrinsic result is a continuous curve in contrast to the ran1 result. In both cases I call the function with random parameters 10,000 times for a parameter value q, add it and then go on to the next q value and call the function 10,000 times etc.</p>

<p>A comparative image of the result can be found here: <a href="https://i.stack.imgur.com/kb5ft.png" rel="noreferrer"><img src="https://i.stack.imgur.com/kb5ft.png" alt="http://i.imgur.com/gZVFdOP.png"></a></p>

<p>If I increase the number of calls both curves converge. But I was wondering: why does the intrinsic random number generator generate this smoothness? Is it still generally advised to use it or are there are other more advised RNG? I suppose the continuous result is a result of the "less" randomness of the intrinsic number generator.</p>

<p>(I left out the source code as I don't think that there is a lot of input from it. If somebody cares I can hand it in later.)</p>