 You're like, oh, it's not that big of a difference, is it? I mean, it's like, that's like only like an inch difference. And this was like, two inches different, right? But that's because the scale is different. You have to know what the scale means, you know, on the left hand side. So that's another kind of thing that could happen. If I was to another one, if I insert another one and make another histogram and we'll like, we'll pull this one down. And pulling it down, okay. And so now, oh, I didn't mean to undo, undo. So now let's get rid of the title on this one and let's click on this side again and let's go over here and say, if we make the minimum, let's say we start the minimum at like 10, you know, so now, right? So now it started at 10. So now these, you know, these, these ones down here are kind of been wiped out. That would be somewhat of an unusual, that looks manipulative, right? To do, that looks a little bit funny unless they were really tall type of ones, right? So then, but again, you could see how that of course would give a different feel than this one, right? You're like, you know, there. So now let's try something where they, we remove the outliers. So let's add another one. Let's select the data again, select the data. And then I'm gonna scroll down before I insert. So hopefully it'll insert kind of down here. And then I'm gonna go to insert. Well, I still got the data selected, but I'm scroll down, histogram, insert and boom. All right, so now let's say, let's say that I go to this bucket size, but I wanna get rid of these outliers, right? That's another thing that might be useful to do, but it also might distort the data a bit, right? Because the outliers, you know, could skew the data. We'll talk more about it kind of later, but it could, but it also is somewhat misrepresenting the data to some degree as well, because they're outliers, that's not the norm. So let's go into the information on the right for this brackets, and I'm gonna leave. Well, we could go from automatic to like, let's say we wanted it at like 2000 as the separation. So that's a pretty decent separation. So you're like, all right, that looks good, but maybe these are kind of messing it up on the outside and kind of making my graph look a little messy. So we could say, well, what if I, well not here, what if I go to my overflow bin, and I say that every, so it's currently on auto. If I make everything that's over like, I'm gonna bring it in here like 77,000, 77,000, then it kind of packs it together, right? So now you've got this, all the stuff that was way on the side is kind of packed in. It's kind of nice to on one side, because now I've got a, now I don't have this data with all these empty buckets in it. But at the same time, it's a little bit misrepresentative because you're like, well, how outside were those outliers, right? And then if I click on this one, the other side, this one's an outlier on this end, let's bring it into like, I don't know, like 63,000, right? So we'll say this one, 63,000. So now you've got everything over 63,000. So you notice that kind of cleans up the data because you're like, yeah, those are outliers. And I just packed them in so that you can actually read the bucket size, which makes sense. But it also distorts a little bit the look and feel of the outliers. So you can see how even when you're trying to be as honest as possible, when you're choosing the bucket size, it can be difficult. And when you're dealing with people who you know have an agenda, meaning they're trying to argue for a particular case, you know that going into it, then you're gonna have to be a little bit skeptical as to how they're gonna present the data, right? So then you gotta be like, well, you know, why did they choose to present the data in this way? Are they really presenting the data accurately? Are they giving you multiple pictures of the data? Or are they trying to kind of distort the data by possibly manipulating things such as the bucket size, the outliers and the height of the graph by manipulating the y-axis. So these are, and this is why again, what you really would like to do is say, I would like to get a look at the actual data and do some of this testing on my own so I can make my own representations of the data and get an accurate idea of what is being said here by it.