Binomial distribution is the discrete version of that. And one little point of notion, you know, these are where the distributions are, this is where they come from, this is how they're related. If you kind of think about, as you get more and more trials, the binomial distribution is going to really approach the normal distribution. But it's really important to think about where these things come from, and we'll talk about it much more in the statistics, because it is reasonable to assume an underlying binomial distribution or a normal distribution for a lot of different types of processes, but sometimes it's not. And, you know, even in things like economics, sometimes people assume a normal distribution when it's actually much more likely that the things on the ends are going to happen, which might lead to things like economic crises or whatever else. But anyway, I don't want to get off topic. The whole point here is just to appreciate, hey, you know, we started with this random variable, the number of heads from flipping a coin five times, and we plotted it and we were able to see, we were able to visualize this binomial distribution, and I'm kind of telling you, I haven't really shown you, that if you were to have many, many more flips and you defined the random variable in a similar way, then this histogram is going to look a lot, this bar chart is going to look a lot more like a bell curve, and if you had essentially an infinite number of them, you would start having a continuous probability distribution, or I should say, probability density function, and that would be, that would get us closer to a normal distribution.