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I am currently working on a project where I generate random values using low discrepancy / quasi-random point sets, such as Halton and Sobol point sets. These are essentially $d$-dimensional vectors that mimic a $d$-dimensional uniform(0,1) variables, but have a better spread. In theory, they are supposed to help reduce the variance of my estimates in another part of the project.

Unfortunately, I've been running into issues working with them and much of the literature on them is dense. I was therefore hoping to get some insight from someone who has experience with them, or at least figure out a way to empirically assess what is going on:

If you have worked with them:

What exactly is scrambling? And what effect does it have on the stream of points that are generated? In particular, is there an effect when the dimension of the points that are generated increases?

Why is it that if I generate two streams of Sobol points with MatousekAffineOwen scrambling, I get two different streams of points. Why is this not the case when I use reverse-radix scrambling with Halton points? Are there other scrambling methods that exist for these point sets - and if so, is there a MATLAB implementation of them?

If you have not worked with them:

Say I have $n$ sequences $S_1, S_2, \ldots,S_n$ of supposedly random numbers, what type of statistics should I use to show that they are not correlated to each other? And what number $n$ would I need to prove that my result is statistically significant? Also, how could I do the same thing if I had $n$ sequences $S_1, S_2, \ldots,S_n$ of $d$-dimensional random $[0,1]$ vectors?

Follow-Up Questions on Cardinal's Answer