And the null hypothesis just says that there's no effect.

And how they can tell us a lot about what's going on in our data.

I am just gonna pause for a moment to let you think about that.

And, I'll show you the correlation matrix in a moment.

And here's the output from that script.

And looked at the correlations among their scores in those different disciplines.

Is r larger than 0? r 0? Or r smaller than 0?

Correlation

TNEF Correlation Key

But just to be clear.

Which I haven't really explained yet.

But there is zero correlation between IQ and emotional empathy, feeling with the other person.

How do we compute such statistical correlations?

Except now we're dealing with two variables, right?

So the closer you get to positive one, the stronger you get the negative one, the stronger the correlation.

Though there's plenty who would.

So the important topics to take away from this segment.

Throughout this whole thing, making toys,

Unfortunately, it's not, because I would like to be taller, but I'm not.

The answer is yes clearly for different values of A, I get different values of B.

So what you see here is a modest positive correlation.

The important topics to take away from segment one is just again, what is a correlation?

There must be some single factor that caused.

The important topics to take away from this segment are validity and reliability.

So why shouldn't we say they have the same symmetries?

So, you might think well, this is just like what we did in correlation.

So for example, spacial reasoning.

So this can be applied to a correlational analysis.

Or here contributing to the numerator in the formula.

If someone, for example, shows you the salary of a person and the age of a person, you can say they're really correlated, or you could say they're not correlated at all.

And we want to come up with a correlation between coverage on one side, and outcome of the run on the other side.

The universality, to repeat, is us that we are the city.

But if I have some degree of co relation, then my predictions get better to the point where if'X' and'Y' are co related at one, then I can get perfectly accurate prediction.

Because what the data shows, above all else, is one thing, which is that success and likeability are positively correlated for men and negatively correlated for women.

They're all members of the same organization.

Then we came to the meat of the unit.

Not just it coincides with the fact that you die and very frequently people in the public get this wrong.

In the middle of this distribution. What that means is if I did the study over, and over, and over again.

So, like X and Y. We've done a lot of examples

level of a group. It does not apply to an individual.

And now the data sits, a square in which data is uniformly arranged, correlated, yes or no?

And now, to sort of revisit the concept or the logic behind null hypothesis significance testing.

That too will depend on the validity of our measures and again in, in lecture five we'll talk about validity in more detail.

In MCMC, that's not true.

And we can test that later.