There's inferential statistics.

That's exactly what we're gonna do in inferential statistics.

That's exactly what we're going to do in inferential statistics.

And we'll start to do a lot of inferential statistics, make inferences.

Just very, very basic probability. And that'll prepare us for inferential statistics.

The central limit theorem, which is essential to understand before doing inferential statistics.

Inferential Statistics, that's a common technique because Descriptive Statistics one, are a lot easier and two, you need to use a lot of the concepts that you learn in Descriptive Statistics to do Inferential Statistics.

Inferential Statistics. So, to give you a layout of today's lecture, it's organized into three segments.

And, this is it's central piece that we need to move forward to do inferential statistics.

And that allows us to dive into inferential statistics which we'll do in the next lecture.

We're not gonna see this a lot, in, inferential statistics or, probably much in this course.

A lot of first courses in statistics start with the distinction between descriptive statistics and inferential statistics.

This last segment focuses in on tools that we'll need as we move forward into inferential statistics.

Most statistics courses, most textbooks on introductory statistics will start with this distinction between descriptive and inferential statistics.

And, as you remember, I talked about correlations as being on the cusp of descriptive statistics and inferential statistics.

So hopefully you found that helpful, and we're going to build on this later on in some of our inferential statistics.

As we move to inferential statistics, we'll use n minus one, and when we do that, I'll explain that subtle difference.

So to wrap up here, the, the tools that we need for, for inferential statistics are the normal distribution, knowing its properties.

And in the third segment ah, I'll focus on some key tools that you'll need as we move forward to Inferential Statistic.

I don't have time to do repeated samples. But I rely upon this idea of repeated samples to do my inferential statistics.

I get one sample, I do my study, I get my statistics on that, I get my inferential statistics, and I move on.

An important part, moving forward in inferential statistics, will be to make sure that we have measures and constructs that are reliable and valid.

So, I'm gonna introduce a new formula and a new concept here, and one that will be, essential as we move forward in inferential statistics.

So today, before we dive into inferential statistics and doing analysis in R, we need to talk a little bit more about measurement theory and sampling.

So, you might want to review that section of this segment over, over and over, just to get that down before we move to inferential statistics.

But it is important to remember, descriptive statistics just describe sample data and inferential statistics are procedures that are used to make generalizations from samples about populations.

We're in Lecture five, the second segment and the last segment of Lecture five and the last segment before we're ready to jump in to inferential statistics.

What's interesting is when you combine the ideas in probability theory with what we know about the normal distribution, then you can start to engage in inferential statistics.

Everything we do or almost everything we do in inferential statistics which is essentially, making inferences based on data points, is to some degree, based on the normal distribution.

But what inferential statistics are all about are if we can do some math on the samples, maybe we can make inferences or conclusions about the population as a whole.

This is so heavily used in, one, it shows up in nature but in all of inferential statistics, I think it behooves you to become as familiar with this formula as possible.

And one of the reasons we're not quite ready to jump into inferential statistics is you saw that the, the validity of correlation based arguments depends on the reliability and validity of the measures that go into the correlational analysis.

Is a statistical procedure that's used to measure and describe the relationship between two variables, and you can think of correlations, we're sort of at a point in this course where we're on the cusp of descriptive statistics and inferential statistics.