 out that this kind of thing, you know, misleading data can happen clearly. And you can imagine many, many scenarios where you have a much more nuanced set of data where the misleading might not be so overt, so clear, but there's clear, there could be clearly some misleading information going on. Now, if I, if I then take a similar set of data, so this is a set of data where we're imagining that there's a score or something that we're taking test scores or something, and we're saying that we want results between one and two. So now your data set doesn't look like all zeros, and then, and then a zeros and two's, it looks like it's got a spread between ones and twos. I didn't actually sort, we didn't sort this data, but you could see that it's basically got a spread of numbers between one and two. So if I looked at this one, and I did our average, you'll note that the average still comes to 1.06. These two set of numbers, if I look at this one calculation, our most common famous calculation, then we're going to get a very misleading number. We're going to say, well, these two sets of data are quite similar. They're not quite similar. They are in one sense, because they all, they both have an average of the 1.05, but clearly, the spread is going to be, is quite dramatically different and could lead to a, you know, hugely different, you know, outcomes or thoughts about what this data is. So this is just simply the average. Again, if I was to plot this on a histogram, even though it has the same average, it looks like this. Now this is the graph similar to, you know, more of what people kind of imagine the histogram to look like, because most people when they see these histograms are imagining a bell curve on top of it. Notice that, that this doesn't have to be the, you know, the graphs don't have to come out that way. I just want to make that clear. And we'll talk about bell curves later. But the major point is that clearly the spread of this data is much different than the spread of this data, even though it has the same, the same average. So keeping this in mind, what this shouldn't do is, is tell us, oh man, statistics is meaningless. The average is meaningless. The world has no meaning. So I'm just going to stop trying to use the tools to derive meaning from the world. No, we just say, you know, tool, the tools such as words, statistics, they can be, you know, they can be used to mislead as well as, as well as to clarify the tools are designed to clarify. And so we have to properly use the tools in order to not make mistakes and in order to safeguard ourselves against people lying with the tools. Again, whether those tools be words or whether they be statistics, it's the same, it's the same thing. They're just misusing, they're, they're misusing the tool, right? If, if, if, for example, you can, you can take the analogy of this versus a verbal argument. And when someone is arguing something, you can more clearly say, kind of see, well, the thing that you just said is, is a lie is wrong. You took a wrong step somewhere. And, and when you, but when you do it with statistics, but they usually start with a, with a, with something that's truth. And there was one, there was a famous thing where there was the multi pythons where they were trying to try a witch and they were kind of