It's the number of successes from n trials, so it's a finite number of trials, where the probability of success is equal to p, so the probability is constant across the trials, for each of these independent trials. So the probability of success in one trial is not dependent on what happened in the other trials. And we also talked in that previous video where we talked about the expected value of this binomial variable, is we said, hey, it could be viewed, this binomial variable can be viewed as the sum of n of what you could really consider to be a Bernoulli variable here. So this variable, this random variable y, the probability that it's equal to one, you could view that as a success, is equal to p. The probability that it's a failure, that y is equal to zero, is one minus p. So you could view y, the outcome of y is really the, or whether y is one or zero is really whether we had a success or not in each of these trials. So if you add up n y's, then you are going to get x. And we use that information to figure out what the expected value of x is going to be. Because the expected value of y is pretty straightforward to directly compute.