So first, reliability.

And it's monitoring their reliability.

That's a pretty good reliability.

So, one, it will depend on the reliability of the, of the variables, so the reliability of x and the reliability of y.

He has a good name for reliability.

I can assure you of his reliability.

That's it's, that's it's estimate of reliability.

We talked about validity, and reliability, and using NH, NHST as a way to get at reliability of correlations.

And, what we're going to do is use that same example and do a test, retest reliability analysis as we talked about in

Matching the reliability, base load is actually irrelevant.

If that, if that were a reliability, reliability estimate in my own research I would not wanna use that instrument again.

Cosby's argument concerns the reliability of the Hette Manuscript.

All he has going for him is his reliability.

I'm going to talk about three methods to estimate reliability.

The reliability of the device has not been the issue.

Just remember the magnitude of this correlation will depend on reliability. Easy way to remember that is the reliability of X refers to inaudible .

And that's because the use of parallel tests and this idea of test retest, they are very simple to understand and, and provide really nice reliability estimates.

So we'll use this idea of classical test theory and these different methods of getting reliability estimates to make sure we have reliable instruments and reliable scores.

So, the goal for this segment is to follow along and write a script where we do a test, retest reliability and estimate analysis by running correlations.

We'll walk through that. That's how we get estimates of reliability.

In other words, reliability is impossible unless there is a natural warmth.

Microscopy, the standard WHO procedure, reaches from 40 to 60 percent reliability.

And in lecture five, we'll talk a lot about assessment of reliability.

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The first is test, re test.

So, in this first segment the focus will be on reliability and validity.

And if the correlation is high, that demonstrates some internal consistency or reliability.

And again nice and high again not great reliability estimates but, but good.

So, the magnitude of correlations critically depends on the reliability of our measures.

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

And, finally, test and test it again.

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