Once again Rubin is being a pompous jerk, Let's look at the details,
>As normally used, hypothesis testing is just plain WRONG.
>That lower-dimensional null hypothesis is rarely tenable,
>and even if it is, such as the speed of light in vacuum
>being constant, it is never directly tested. Also, one
>has to balance incorrect acceptance, whateve that means,
>with incorrect rejection.
Ok. So we must appreciate the danger of type I and type II errors, Who
says we do not? Not me. And what alternative do you suggest to hypotheses?
Just saying "Rubin said it was so!" and letting the world bow down?
>
>>>In practice, you cannot do this exactly, as it would
>>>require an infinitely large and infinitely fast computer
>>>operating with zero cost. But it does tell you that much
>>>of the current statistical religion is wrong.
>
>>This warning should be sent to the traditional structural equation
modeling
>>folks using LISREL etc. They are the ones who have the problem with
>>ambiguity. With CR the data support only one model, unlike the case with
>>LISREL.
>
>The data can never support only one model.
You are simply wrong, And if you bother to read my paper instead of
reminding us how famous you think you are, then you would see that CR pumps
out a single model of causation.
>
> If you are simply suggesting that we have to know everything to
>>know anything then you are saying that only an omniscient God can have
>>anything to say in science, I do not think things are that bad, There is
a
>>reasonable faith that keeps science from grinding to a halt because we do
>>not understand every tick in the Laplace clockwork universe,
>
>The Laplace clockwork universe is not even accepted by many.
>While it is not possible to do things precisely, one can
>make good approximations.
For the record, I do not accept the Laplace model either, It is just a
useful model to refer to when talking with people who expect omniscience
from science,
>
>>> The polarization effect is there. Have you paid any
>>>>attention to the posts on corresponding correlations/regressions?
I see you did not bother to answer this one, Dr. Rubin, Afraid or just
lazy?
>
>>>WHY should one look at correlations or regressions?
>
>>Because we can not do experiments on many of the variables of interest in
>>the social sciences, Manipulation of these variables may be impossible or
>>unethical, So we have to make do with measurement and
correlation/regression
>>methods, I cover something of the history of this problem in my latest
>>paper, with a bit about the place of astronomy as a nonexperimental but
>>mathematical science,
>
>So what? There are many situations in the biological and
>social sciences where current theories are all nonlinear.
>Are there any cases where the analysis of a set of genes IS
>linear? There are many ways to analyze data not relying on
>correlations and regressions.
>
I am interested in linear relations, I am also interested in the social
sciences, You appear to have learned how to measure nonlinear relations in
biology, I am sure you feel almost like a real doctor (MD). I will bet,
however, that you can not measure nonlinear causal relations without an
experiment, And I also bet you prefer to talk in nonlinear ways in order to
avoid being caught in logical inconsistencies,
>>>Are
>>>these linear relations even approximately correct? Using
>>>linear approximations is reasonable for SMALL effects, but
>>>those using correlations and regressions usually have large
>>>ranges for their variables.
>
>>First, larger variances are desirable in correlational research, It is why
>>use factor analysis, The eigenvector/value solutions maximize variances,
>
>Do you understand factor analysis? Anderson and I wrote one
>of the few mathematically sound papers on the subject, appearing
>in the proceedings of the Third Berkely Symposium.
Listen to me little man, You have no idea of whether or not I understand
factor analysis, Go get Nunnally's book Psychometric Theory, Read it,
especially the section on factor analysis and the selection on items in
questionnaires. Hell go get any book you have not written on factor
analysis, Read about eigenvalues, Now tell me, why does the first component
have the largest eigenvalue, the second the next largest, etc.? It is to
maximize variance with as few variables as possible.
>>What you are saying, however, is that it is pointless to look for linear
>>relationships because they do not exist in nature,
>
>They can be useful approximations, or the can be poor. In
>any case, they must be justified.
Where have you ever justified anything you have said in any conversation
with me or any thing you have said about corresponding regressions?
>
> I do not think things are
>>that bad, And certainly 90% or more of the statistical analyses done
assume
>>the possibility of linearity,
>
>Practitioners of ritual abound; most find it unable to
>leave the position, no matter how easily it is shown to
>be inappropriate.
What are you talking about "ritual bound?"
> Some would even say that nonlinear
>>relationships can be broken down into a set of discrete linear stages or
>>subsequences,
>
>This is nonsense, unless you allow an infinite set.
Please explain, Again, you seem to be suggesting the we must know everything
to know anything, Far out, man,
>> Such slicing may be better than just giving up on linearity,,
>>especially since most people cannot understand linear theories, much less
>>nonlinear ones,
>
>I see no reason why a non-linear theory is any harder to
>understand than a linear one. Most physical relationships
>have major aspects of non-linearity.
Well its like trying to have a conversation with you, You will not
communicate linearly, I make points A, B and C and you jump to the square
root of your aunt Mabell's little toe, Talking with you is like talking to
a person who is on pot. You skip around. Throw out pretentious sounding
points without any explanation and then laugh at how clever you think you
are, I do not care if you had sex with Anderson and the rest of the boys at
the Berkely Symposium. Do you or do you not see how polarization works
within the simulation data I present, Do not go via Inverness, Scotland to
answer this question, Do not tell me nothing is linear, I am not asking you
about the world, I am asking you about the random numbers and logical
relationships in the model y=x1+x2.
Now grow up and have a conversation with me, How in the world do people
like you get jobs in universities, Its got to be who you know and they have
got to hate science.
Bill
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