Statistics textbooks, whether they're in Psychology, or Economics, or Sociology, or

I don't get repeated samples.

And then once we build our toolkit on the descriptive statistics, then we can start to make inferences about that data, start to make conclusions, start to make judgments.

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

Well, I'll kind of group them together.

When we're doing just descriptive statistics on samples, we'll use n in the denominator for these formulas.

We know what percentages scores fall in certain uh, segments or intervals of the distribution.

So today, in just one lecture, I'll provide you with an overview of the essentials of Descriptive

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

And that's when I'll move from the broad view of Histograms and distributions to this more ah, focused view of

All of that together, comprises, what's known in statistics, as the central limit theorem.

Fourier analysis, except that now their waves are in a sphere, and no a line, or, in a plane.

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.

I'll also talk about percentile rank. We'll cover a little bit of probability.

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Because galaxies are very far away.

An amount with which the precision of a measurement can be estimated.

The central limit theorem, and this is, was developed by a statisticians and is used in applied statistics to do inferentials.

So sometimes a correlation can be thought of as just a descriptive statistic, we're just describing the relationship between two variables in a sample.

Statistics suggest that the population of this town will double in five years.

Welcome back. We're on segment three of Lecture 2 on descriptive statistics.

It's a theory, anyway.

Let me guess.

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

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Take a guess.

You got a guess?

We need to state the facts.

That would be descriptive statistics.

Welcome back.

And the first one we'll, we'll deal with is that distribution of sample means.

Linear regression? Do we compute confidence intervals?

But it does it for everybody uh, so it's just a little bias in the measurement uh, that's across the entire sample.

I'm guessing.

I don't presume to...

That's my guess.

I've surveyed a sample.

Take a guess, Mr. Gaeta.

The clock is a guess.

Well, I didn't learn it in school. That's my best guess,

which i suppose i deserve.

It's just a conjecture.

That's just guesswork.

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.