Bungee Jumper

Do you really think that if you keep calling it "commonly accepted statistical principle", it's going to magically become so? No matter how many times you say that...you're still going to be laughably wrong. No, it's not historical fact. It's pretty much some theory you've concocted.

Only if the range of error is the same as the range of possible test scores...because, again, all you're describing is how the error evolves. The error and the test are two completely different things. It's also irrelevent to the topic that started this nonsense: testing the IQ of the same person twice is not the same as testing the IQ of children and comparing it to the IQ of their parents...something else that you are yet again too stupid to understand. This all gets back - again - to the simple fact that you don't know what measurement, error, regression, mean, or variance mean.

I could say that myself...if it weren't for those damn flying monkeys.

Jesus Christ. Is there anything you're not a total idiot about?

Never seen a nightgown with a crotch before, myself. But then, I don't wear them nearly as often as you do...

What little idiot brain cell rattling around in that confused skull of yours gave you that ridiculous idea?

BUT LUCK IS NOT ERROR, YOU IDIOT. You don't know what you're doing, simply because you have absolutely no idea what "error" is. Which is why you don't know you're measuring it, which is why you think you're getting a result that you are IN NO WAY getting.

Oh, no. He understands that. He's already pretty much stated that "chance" and "error" are the same damn thing.

And that's because your simulation is entirely !@#$ed up; as I keep saying, you're measuring the regression of the normally distributed error, but over a data set that you've managed choose in such a half-assed manner that you're not even doing that right. You're choosing a threshhold so that your overall error is overwhelmingly giving a net positive bias...which means the regression toward the mean of the error you've selected for is of course going to have a negative bias, which you mistakenly attribute to the normal distribution of the IQ scores itself. This is because YOU HAVE NO IDEA WHAT YOU'RE !@#$ING DOING! It's also why I need time to do it properly: a correct model of the system isn't a normal distribution of discrete values that each have a normal distribution of error applied after the fact. A single data point under the gaussian, in other words, doesn't have a value of X (140, if you prefer). It has a value of X*exp((e-E)^2)/2*sigma^2, where e is the measured error and E is the mean error (or thereabouts...like I said, I need time to do the math; I haven't had time yet). Like I also said earlier: do the math. Don't do a half-assed "Monte Carlo" (sic) simulation. DO. THE. MATH. Of course, you can't do the math. That would require reading a textbook, which you can't do either, it seems.

I understood the one where he said "measurement error doesn't cause regression toward the mean". Puts me one up on you, at least.

The population's mean height? Or the mean error of your measuring system? Because your "Monte Carlo" simulation (and please stop calling it that; it's an insult to people who've done real ones) proved the latter. If your measurement error is normally distributed, your measurement error will regress toward the mean of the measurement error. Again, as I've been saying...you're working with two normal distributions, and confusing the regression of one with the regression of the other. And THAT is why you're a !@#$ing idiot. Because you can't tell the difference between a normally distributed data set, and normally distributed error within the normally distributed data set. That's why I was so damned careful in defining the parameters of the example in the other thread: because I have to demonstrate that there's a normally distributed set of normal distributions at work (i.e. there isn't a normally distributed set of distinct IQ scores, there's a normally distributed set of Gaussian distributions representing the measurement error of each data point), and I have to demonstrate, as everyone including Wraith has stated, that it's the main normal distribution and not the error causing the regression. Not that you'll be even remotely smart enough to understand that...but I'll do it anyway, since I find calculus entertaining.

You would if you READ A TEXTBOOK. If Wraith supports your opinion...he's wrong. But he doesn't support your opinion; I've read his posts.

Except that that isn't any form of regression toward the mean, you dolt.

You're right. You didn't say measurement error causes a rubber band to snap back. You said it causes the rubber band to stretch. Which is even more absurd.

Then don't take my word for it. READ A DAMN TEXTBOOK. Let's see some action--math, links to sources that actually support you, something. Because you've done absolutely nothing this whole discussion except create a whole lot of hot air. 859048[/snapback] And I haven't gotten to it yet. The answer's obvious; the math that proves you're an idiot is, however, rigorous.

We're NOT arguing the effect, we're arguing that it's NOT caused by "measurement error", you idiot. It's caused by what we all - including Wraith - said: the normal distribution of scores about the mean. Extreme values in a normally distributed sample tend to be less extreme if measured again not because they're in error, but because they're extreme, and the probability distribution of the sample dictates that there's a VERY high probability of getting a lower value than an equivalent or higher value for a given extreme measure. AND THAT'S NOT ERROR, YOU !@#$ING IDIOT!!! Refer to my dice (plural, NOT a single die, which does NOT represent normally distributed probability, hence is not applicable) example, or ANY CREDIBLE STATISTICS SOURCE ON THE DAMN PLANET, YOU LOON!!!!! Like a textbook, maybe.

I provided a link. To a book that "supports my position". (Though it's not "my position"...it's math.) And I've found several articles from far more credible sources that "support my position". You haven't. There's a very simple reason for that: you're a !@#$ing moron.

Link AGAIN. Conversely, if your view were correct (it's not), you'd think you'd be able to describe it in terms of "variance". But then, you can't even define "variance".

Don't forget hyperstats.

I think, when you post for all to see that "error causes a stretched rubber band to snap back", we don't need him to decide whether or not you understand him.

Link, again.

No, because it's too expensive. Moron never heard of !@#$ing libraries, apparently.

That's not what he said, and that's not true anyway. As usual, you're not even remotely capable of understanding the concepts.

Yes, yes, yes, all, and yes. Usually, though, it's a charge of "fetal homocide" or some such; in every case I know of, the death of an unborn baby when the mother is murdered is recognized as a crime distinct from the mother's murder, and distinct from "regular" homocide as well. In other words, the law recognizes the difference between the murder of a fetus, and the murder of the pregnant woman, and as such is actually not as paradoxical as it first seems.

Or something almost exactly unlike it. Again...how can you say you "intelligently" discussed something, when you can't even define the words you're using. Tell us what "variance", "error", "mean", and "regression" are, genius...