 I think we'll start this out with the demo. We've got the Hilbert chain as you're going to have eight hands. Under my fist, loud! You grabbed by the function, full of iron. Yeah, I see it. We might be wrong. We're funny, but not always a joke. We're nervous. The oft overlooked things in our field is, where does behavior come from? Right? A stork. Right. Right. A storks don't make that sound. Whatever. Thanks. Great. He's generating variability in my behavior. I wonder, folks. So where does behavior come from? Is it inside the organism? Is it outside the organism? Where is it? And what happens? So we need to address this. So one of the issues that I run into, or that I run into that we all run into, is that we're talking about how, we talk about applied behavior analysis as if it's almost perfect. The problem is it's not. So I'm guilty of this. I almost get dogmatic about our field sometime. I'm a bit guilty here. But I think the point that we need to remember is that we're good for a reason. It's because we consider everything we need to consider. And then we do things like all these other videos on experimental design to make sure we know what the heck we're talking about. Then we apply it in the real world. We call it applied behavior analysis. And we continue to test our stuff in the field and make sure that we refine it in that setting. So we kick butt on making sure that our independent variables are effective, not only in the lab, but also in the real world. But a key component for all of our stuff, we've talked about before. Baselines, they're not perfectly stable. They have variability. So where does that variability come from? Well, there's typically speaking five sources. Now you can break those five sources, probably down to 20, which I'm not going to do today. So we're just going to break it down and give a little bit of topics that we need to realize or that we need to address. Because when you're out there in the real world working with real behavior, it's not pretty. It's ugly. It does not look like the books. Experiments that demonstrate this stuff that have been published. One of the reasons they got published is they are so crystal clean that they show the effect perfectly. That's so beautiful. But you're not running those types of experiments in the real world. So you've got to be ready for these different types of things. So before you give up on a particular intervention, think about where the variability and where some of the issues are coming from, which is kind of what this video is about. So let's back up a little bit. Some people incorrectly think of our field as supporting the concept of tabula rasa, black slate. We are not a blank slate field. No one that understands our field would argue that an organism is a blank slate. You come to the table with something in here. You're born with stuff inside of here. And as a result, you engage in behavior. So when you engage in behavior, that's what you as an organism are trained to do. That's what you're born to do. You have a certain amount of preparedness when you come to the world to engage in behavior, whatever it is. You, as an organism, bring that to the table. It's not a blank slate. You engage in behavior and the environment acts upon it. Okay? Subsequently, that's how we get to all the stuff in the rest of the field, right? So reinforcement, behavior goes up, punishment behavior goes down, extinction goes away, stimulus control, all these other things. But you have to engage in the behavior first, right? So that engage, that emitting of behavior is literally the operant conditioning field. So when we talk about the sources of variability for behavior, why is my baseline not perfectly stable? Well, the organism, you are not perfectly stable. Things change, right? You could be in different states. You could be hungry. You could be sad. You could be tired. You could be awake. You could be excited. You could be in front of a camera. You could be all sorts of different things. Well, that's not right because that was an environmental variable. But basically any state that you as an organism exist in can change your behavior in some way, shape, or form, and that's fine, right? So that's one source of variability. More sources of variability, experimental settings. The setting under which you're conducting the study can prompt different behaviors to happen, period. So in this particular setting, right, in this environment, I engage in behaviors in certain ways. I do different things that I might not do outside of this environment. The same thing happens when we're doing experiments. If we set things up in this really rigid, very cold and so on and so forth laboratory, then guess what? That's going to happen in a laboratory and it might be different if you set it up in a nice warm fuzzy laboratory, right? I don't know, right? Another funny one, not funny one, another one that we always forget about that I think is really important is measurement. If your measurement tools aren't perfect, then you're going to get variability in your behavior in your measurements just because your measurement tool sucks, right? If you're not observing everything really well, it's like, you know, maybe I'm counting the number of times it does something back there behind the camera, but I'm doing this. See, I kind of look at him once in a while. And other times I look away. Oh, there he's doing it again. I look away. And maybe the next series of measurements I'm really consistent. I make sure I watch him really well. I keep a good eye on him the whole time. I never look away in all these sorts of things. He's doing funny stuff to me right now. So anyway, I keep an eye on him, right? So you see that my measurement tool changed, right? Or the quality of my measurement tool changed. My measurement tools are a source of variability. Experimental design itself creates variability. That's pretty obvious. That's kind of the whole point of experimental designs. Interestingly enough, another one is data analysis. How you analyze the data can totally see how much variability you have in it. Are you looking for response rates? Are you looking for total responses? Are you looking for cumulative records? Are you aggregating things and talking about average numbers of responses over a given period? Your unit of analysis is what's important here. So all of these different things affect the variability of behavior that never really had anything to do with your independent variable, right? So keep that in mind that one of the reasons that nothing's perfect on the graphs that you're going to get is because all sorts of stuff is affecting the variability of the organism, which means we need to look for general patterns of change. And we need to do our best to control variability on the things that we can, right? Because some of those sources of variability become issues for bias. They become issues for intervention issues. They become issues for confounding issues and all sorts of different things that come about as a result of allowing too much variability, right? But we have to know where that variability comes from in order to do something about it. So probably not in a typical book that you would find about behavior analysis unless you really get into the research methods stuff, but something that I really, really, really wanted to talk to you about because we see it all the time in the real world and people often don't understand that you're not quite ready for it. So there you go. Lots of sources of variability. I implore you to like and subscribe to our channel. It is extremely important that you do so, especially since you want this content. 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