 Chapter 7 is called Estimation, and this is our first real inferential statistics chapter. This is where we're moving from describing individual samples to making conclusions about the populations that they came from. It's trying to go beyond the data and talk about something bigger, a larger group, a different group, something in the future. But Estimation is the first procedure we want to talk about. And what you're trying to estimate is a population parameter based on a sample statistic. And so what we're going to talk about in this chapter is what kinds of estimates you can make. There's a point estimate and a confidence interval. Point estimate is just a single number, and it's really the same as the sample statistic. But a confidence interval is where you give a high number and a low number. You say, I think that the population is probably somewhere between here, and you say how confident you are about that. It might be 80%, 90%, 95%, and you calculate that. It's not just how you're feeling about it. And we're going to go through a number of work solutions to point estimates and to confidence intervals and talk about some of the things that influence the precision or the width, how much variability there is between the high and the low end of a confidence interval. That's things like the standard deviation of the thing you're talking about, the sample size, the level of confidence that you want, a number of things. But the point of all this is it's going to take us from descriptive statistics to inferential. Now, in terms of why this matters, well, you know, this is something that you do all the time. You do it when you wake up in the morning and you're getting ready to leave the house and you look out the window and you look at the weather and decide, do I need a coat? Do I need sunglasses? Do I need an umbrella? What do I need? Because you have this one sample, looking outside your house and maybe by the time you get to work that might be a different place or a different time are things going to be the same. You're trying to make a conclusion based on this one observation outside your house in the morning to what it's going to be like in another place throughout the day. That's estimation. Now, we're going to use it because estimation is a common procedure in a lot of fields. It's very common in medicine, it's common in economics. In the social and behavioral sciences, we more frequently do something called hypothesis testing. That's what our other chapters are going to emphasize. But this is often a good procedure. It's a little easier to start with because it doesn't use some of the pretzel logic of hypothesis testing. Now, in terms of what you might actually use this for. Again, you do an intuitive version of this all the time. Let's say you're trying to lose some weight. You know how much you weighed in general at the beginning, but you know what fluctuates around a little bit. And then let's say you're doing diet and exercise, maybe something else to try to lose weight. Again, you know your weight fluctuates a little bit. You're trying to decide has your weight changed or really what's your true weight now based on, say, three or five days of observations. You're trying to estimate the population value, your true weight at that moment. Or you're trying to estimate how hard is it to work on home improvement projects or if you take the freeway to get to work or to school or to home, maybe you're driving up to the freeway and you can see the traffic on it. I can when I get on the road. And if the traffic's moving really slowly, you have to estimate is this how it's going to be for a really long time? Should I pull off the road and go some other way? Or is this maybe a small glitch that will pass? You're trying to estimate what the traffic going to be like on your entire commute. And so these are intuitive versions that you do of this. You know, if you can see a lot of the freeway, you can make a better conclusion than if you only see the entrance ramp. That's an idea of some of the things that influence your confidence and give you an idea also of how you can use confidence intervals in your real life and how they connect to the other things we're going to talk about in this class.