 When I look at the height of the graph, it's only going up to 3.5, whereas the height of this graph is going up to like 40, and the height of this graph is between, it's like 30 to 40. So notice, the other thing to kind of keep in mind here is that, is this y-axis? So, because this y, see it might look like this bar is as tall as like this bar, but it's not because they scaled the y-axis in order to fit this set of data. So that's another thing that can be a little bit deceptive when we try to change the data. Now, this one, we went a little bit extreme, on very extreme, on the number of bends to emphasize the point that more detail isn't always good, because of course, I can put this all the way down to one, right? There's only a bend width of one. Excel might not let me do that, but you can do that in theory, and that would be like entering just a normal bar chart, right? We just basically had all of the numbers down here, and you would have very few of the numbers where more than one happened to hit exactly on that particular number. And that means the picture wouldn't be very useful to us in that case. So this is gonna be most likely to spread out, and there's not a, only three in one bucket isn't, the buckets don't seem large enough to really give us an idea of the data, although they still give us kind of a spread in this case. So then let's take a look at the next one, and this one, what we did was change the height. So just to emphasize the other thing that people could manipulate, is you could change the height of the histogram. And so what that does then, now I've increased the Y up to 100. So even though this is still going up to like 30, you know, it's 25 to 30, it looks like a more squat histogram, because if I compare it to this one up here, where this one was going up to a similar region, it, this one looks a lot bigger because the Y went up to 30 as the maximum number, which makes sense because Excel's gonna try to make the, to zoom in as much as possible on the data that is relevant. And in here we have a whole space on the graph that isn't relevant. So Excel would normally kind of oftentimes trim that out by default, but if someone wanted to de-emphasize the height of this graph, you know, they might try to add, you know, you could try to, someone might try to add a little bit more on the Y axis and say, yeah, well that's pretty squat. It's still pretty, you know, it's a much more squat curve. If you look at the curve here versus this curve, like look at that, it's a huge slope versus this one, you only got this little squat thing. Well, no, it's like the same. It's just that you changed, it's just that you changed the representation on the Y. See, and then, and so this one, let's take a look at this one, where we did the starting point at 10 instead of zero. So that's another, so if you started at 10 on the bottom, that's kind of weird for a histogram oftentimes, because oftentimes you're gonna start it at zero. But if you started at 10, then it kind of trims out all of the other, all of the low points, right? See up here, we started at zero, and then you had these ones over here. If you started at 10, this, this, this, and this are all gone, right? And you're only starting at 10. Now maybe that might be relevant sometimes, like you might say everything under 10 is irrelevant, so I'm just gonna start it at 10, but that's kind of unusual, right? And clearly that's emphasizing the middle range and all of the lower numbers have been cut out. So that would be kind of a misrepresentation of this data. You would think it'd be a little weird. But, and then this one, and this one, what did we do on this one? Oh, I see, see this one is where we did the overflow bins. So now this one up top, you'll remember the normal one had these outliers on the end. Now you might do this for legitimate reasons because you might say, hey, look this, the outliers often of course are the problem with the dataset because when you look at an average, the outliers are thrown off the average. If you compare the average salary of people that live in your neighborhood and you happen to have a millionaire that one mansion that sits like in the neighborhood and everyone else makes normal salary, then if you count that outlier, it's gonna pull up the average a lot because the outlier is quite extreme. So the outliers are often an issue. So if you make the histogram, like what if these were 0, 0, 0 bucket and then you had like a one way out here? Well, then it's gonna really skew the look of your graph because you have to include that one way out here. And so to trim that out, you can use these this function. So now you can say, okay, the bandwidth is 2,009 bins, same thing we had before, but now in the overflow bin, we're gonna say everything that's above 77,000 and everything under 65,000, we trimmed those out. So you can see now we have a couple in this one because those two that were here, we kind of combined them together in that overflow bin. And that gives you a more, this is kind of the nicest looking one, possibly of all of them because it kind of shows the data nice and trim in the middle, but it de-emphasizes the outliers. We're not getting a good sense of what those outliers actually are because we've kind of pulled them in to everything over 77,000. So if you have an idea of, well, I wanna know how big that outlier actually was or whatever, then this is kind of removing that data. But again, you can see why sometimes that would be useful to do to get a pictorial representation that's kind of focused on the heart of the data. So clearly, when you look at different data that might have a different histogram to it, then as you adjust the bucket size, the main things that you could adjust the bucket size, the number of bins and the outliers, and then also you could play with the Y-axis will have different kind of in-facts and different on different data sets. So of course we have to be aware of, which is why it's nice to look at the data from multiple different angles. It would be great to have multiple angles. So if someone is having a deceptive representation of the data, then you can call it out and say like, that looks a little deceptive. Not trying to call you a liar or anything, but it kind of seems like you kind of seems like you did some funny stuff to the histogram. I'm not sure if we looked at it from a different angle, we'd get the same sense.