Okay, so either you got sensitivity down or you just skipped to this section. If it's the second, thanks for making this tutorial that much more difficult to write. To summarize, sensitivity is one of the conditions necessary for chaos, and is basically the concept that even minor differences in initial conditions of a system in chaos cause dramatically different results to occur in that system over the long run. It is often summarized in an interesting phrase called the Butterfly Effect.
Now, let us look at periodicity since you already know about sensitivity. It is the idea that some patterns repeat in the system after a while. The image on the right shows the Lorenz attractor, developed from plotting weather patterns and it displays this periodicity as well.
You will probably need a better example than a weather system to understand mixing when we get to it, and yes I slept through meteorology as well. The best such example is to look at quadratic functions. How can quadratic functions be chaotic, you ask? That's what people at Los Alamos were wondering about Mitchell Feigenbaum when he came to do research there in 1974.
Using the Feibenbaum example let us look at mixing. Mixing is basically the idea that given enough runs of iteration, the system will go through all sets of intervals. Confused? Okay, let's divide all numbers between 0 and 1 into ten sections: between 0 and 0.1, etc. Let us pick the interval between .3 and .4. Now, using the chaotic function of f(x) = 4x(1-x) and having our starting value as .21, let us iterate. We get .6636 as our first response, which is not between .3 and .4, so let us continue. Plugging in .6636, we next get .8929 (I rounded for display here, but it is still to the proper decimals on my calculator), still not in the interval. The next number is .3824, which is in the interval. Go back to the example that I provided and test it out again. It will work with smaller intervals too, but it might take a while longer if you want to try it.