 Okay, we're gonna look at an example of an experiment and to assist us or to be more efficient. Now, if we were all hanging out together, you know that we would, all of us could come up with different hypotheses and we could all like form groups and then share out our examples. But because we're not all sharing space, I'm gonna attempt to make this a little bit easier. I really encourage you to sort of predict, like use this video here to predict the different aspects of the experimental design that we're gonna be talking about. So before the answer comes through, like push pause and then holler out because the doggies are really good at hollering out while you're in a lecture with me on video. Get your kids to join in, get your partners, people at the library, like everybody can participate in this interactive process through this video. So I came up with the hypothesis and this is, I wanna look at what we expect about a hypothesis, like how do we know what makes a good hypothesis? And we've talked about it a jillion times. So let's, we can check ourselves to see if this is a good hypothesis. My hypothesis is house plants can grow in red light because all light contains red wavelengths. And so the red light won't matter to chloroplast function. There's a couple of things I wanna bring out or point out in this. Number one, this is explanatory. There's information in this hypothesis. We haven't talked about plants before, but now you know that all light contains red wavelengths. That information is included in the hypothesis and chloroplasts are involved in light. Chloroplasts need light to keep plants alive. We've got those pieces of information that make this hypothesis. It's not just a random, well, it has explanatory power, which is good. The next question we should check is, is it testable? Do you think that this, can you think of a way to test this hypothesis and push pause and think it through? Like, how would you test this? Just come up with an idea. I wouldn't have come up with a hypothesis that wasn't testable. So by the end of this, you will see that, yes, indeed, this absolutely is testable if you're kind of going like, I don't know how it is testable. And is it falsifiable? How do you know if the answer is no? They can't grow in red light. How are you gonna know if that is true? Well, they might die or they're not going to have as robust growth. They might have fewer leaves. There are things that are going to happen to them that will indicate that they are not thriving in the red light. Now, let's identify the variables. Remember, before I even do this, the independent variable, one thing, what is the one thing that we are changing here? What is our one independent variable? In this case, the independent variable should be light color. And I'm imagining that we would have regular light and red light. And those would be our two groups that the plant is either gonna get regular light or it's gonna get red light and we're gonna compare. I'm going to have a suggestion for possible, another possible thing for us to consider. Our dependent variables are all the things that we can measure to indicate growth. And again, like, dude, we can go all day long. Leaf biomass, weight, height. I said leaf biomass, that implies like the amount but like maybe, like, well, I guess, I want like surface area, leaf surface area. I want that included in there. We could go all day long. I guarantee I'm not thinking of all the things that are dependent variables that we could control or that we could measure and draw conclusions. And then what are the things that we wanna control? In all the groups, every single plant, what are the things that are gonna be the same with every single plant? The type of plant. We gotta keep the type of plant the same, right? Probably the age of the plant. Maybe the size of the plant. Going at, you know, maybe a three week old plant. We wanna pick the three week old plants that are all the same size because some of them are bigger and some of them are smaller. We want these qualities controlled as much as possible so that we know when we make our changes, it's based on light. Now, now is the part where we need to talk about our controls. And this is a really good example. Again, push pause and think about those controls because I think we have a good example of a way to have a negative control and a way to have a positive control. I already said that we should have a group of plants that have regular light. I would argue that a plant with regular light is gonna be our positive control. Do you agree with that? And I'm making that case because that regular light is gonna show us the kind of growth that we would expect if everything is just normal. Light is light, then if the plant grows just as well in red light as in regular light, then we will have our answer. The other, how could we do a negative control? Well, what would it look like if the plant didn't do well in the red light? We could have a negative control of what? No light. That would tell us what it would look like with our plants not having any light at all. Here's our design. Now we're ready to take measurements and collect data and look at the numbers in those dependent variables that we've identified. When we collect data, when we take measurements, this is going, for me, our last two sections are the least inspiring part. I would love to just stop it here and then just be like, yeah, yeah, yeah, measurements. Know how to do them. And yeah, yeah, yeah, graphing, do that too. I am going to suck it up, me, and talk through units of measurement and why it's important and how we move in and out of units of measurement, like how we convert between units. And I'm gonna talk about graphing as well. So I'm not done, but now we're into some lab detail stuff that's probably important skill-wise, but you can tell that I'm really looking forward to it, huh? Okay, I'll be right back.