Perhaps it will help you to know how sampling actually works:
When light is hitting a (let's say) diffuse surface, it is scattered in all directions. Each photon will leave the material in a different direction. Since the sun emits about 10^45 photons each second, consider this as an infinite amount. According to this, the following picture should have an infinite amount of arrows:
Calculating the color of that surface point is basically an integration. (Let's leave out more bounces since each bounce of each ray has also to be integrated). Since there is no way to calculate this at all, Cycles uses the Monte-Carlo integration as an approximation of the end result.
Short version of the Monte-Carlo-integration: Use random sample points and integrate them (in this case by averaging them). According to the Law of large numbers, with time the result will converge.
But taking fully random points isn't that a good idea. So, Cycles instead uses a Quasi-Monte Carlomethod. The difference is, that it doesn't choose its sample numbers (pseudo-)randomly, and replaces them with less random, but better values. This makes sure for example that no region will be sampled significantly more often than others.
The pattern for getting the random values Cycles uses is called Sobol. Compare the two images in the the Wikipedia article and you can see easily why it is better suited. The pattern of Multi-jitter sampling looks really similar to the sobol one, that's why the difference isn't that noticeable.
Each pattern has two important properties: The speed at which it converges (the number of samples you need until it's more or less noise-free) and computation time. How the sequence converges depends heavily on the data you are integrating. That's why some scenes work better with one than another.
Sadly, I can't tell you how to chose your sampling pattern beside trying it out (yet).
The paper about Correlated Multi-Jitter says:
We have presented a modification to jittering and multi-jittering that greatly
reduces the noise in rendered images at higher sample counts while gracefully
degrading to unstructured noise at low sample counts.
I found this comparison of both methods showing that there is a difference, but it is barely noticeable (to me, in that scene).
Also note from that answer:
For Sobol this works if the number of lights is a power of two, for Correlated Multi-Jitter it works for any number of lights.
This will affect how you set up your sample count depending on light count. For branched path tracing, it is different too.