I have searched the trilogy for evidence of the use of this phrase and found none.
I have changed this to read 'randomness in the probability wave distribution.' Meaning that an event can have a few, or many, possibilities in a probability wave distribution. So there can be a lot of randomness around an event if there is a large probability distribution.
US Space and Rocket Center (Part 1 of 3)
https://www.youtube.com/watch?v=IcxeEaO ... ibCRsMi_IM
I want to explain to you what it means to say, “Take a random draw from the probability distribution.” You hear me say that phrase over and over again. And most of you probably think that there’s a bunch of possibilities out there and you randomly just reach up and grab one. That’s not what’s going on at all. In that case every possibility would have the same likelihood of being drawn. It doesn’t. That’s why we have the probability distribution.
Probability curve.jpg [ 151.12 KiB | Viewed 976 times ]
We have twenty-six possibilities here. So everything from A-Z is a possibility. A is a possibility, B is a possibility, and C is another. We have twenty-six different possibilities that could happen. This is a probability distribution. This line which goes up here and here and here and so on and back down is the probability distribution. Many of you think of probability distributions as either being uniform in which all of the possibilities have the same probability or Gaussian which is the bell-curve. It starts low and comes up and goes down. Bell curves are nice, neat, easy things to work with. Real probability distributions, that describe the real world, can be any kind of ugly distribution. It doesn’t have to be something nice, pretty or symmetric.
This probability curve I made up. And that means that there’s one 'A' here. That’s what the one is. There’s two 'Bs' here. That’s what the two is and so on. There’s six H’s here and that’s the six. Each one of these letters has a probability that’s represented by the number of those letters that are in here. A probability distribution gives the relative probability between the different possibilities. So this 'K' is 25 times more likely, more probable, than the 'D' or 'E' which have one. Look at the 'L.' It’s seven times more probable than the 'G' because here’s the 'L' and it’s at 21 and here’s the 'G' at three. So it’s seven times more probable than 'G.' It’s just simple arithmetic there. So this is an example of a probability distribution.
Here’s an example of how a random draw is taken from the probability distribution. There’s 165 letters under this distribution. That’s one, plus two, plus two, plus one, plus one, plus two, plus three, plus six, and so on. You keep doing that all the way to the end. Count them and you get 165 letters. I’m sure somebody’s going to count them and see if I calculated it correctly. Put all 165 letters in a box, shake them up, turn the box upside down, and shake it again. Reach in and pull out one letter and that’s the random draw. Not all letters are as likely as others. The Z, the D, and the E are very unlikely because they only have one letter out of 165. You could pull one of those letters out, but it’s unlikely. You’re most probably going to pull out a K because there’s 25 of those there. That’s what I mean about the most probable thing being the most likely thing to happen. So to take a random draw on the distribution means that you pull things out according to the probability.
What’s the probability that you’d get any one of these letters from A-Z? One - because you’d take all the area under the curve and divide it by all the area under the curve. And if you reach into the box you will pull out a letter. The probability of getting one of the letters is one. The probability of getting any particular letter depends on the relative probabilities between the letters.