All of your observations about the deficiencies of data are perfectly
valid. But what do you do? Just give up because your data are messy, and
your assumptions are doubtful and all that? Go and dig ditches instead?
You can only analyse data by making assumptions - by working with models
of the world. The models may be shonky, but they are presumably the best
you can do. And within those models you have to assume the data is what
you think it is.
I agree that we do not, in general, make it sufficiently clear to
students that all statistical analysis deals with models, and those
models involve assumptions which are frequently heroic - but you do have
to get down to doing some analysis at some time, you can't just whinge
about the lousy data, and to do that analysis you pick the techniques
appropriate to the models you are working with.
Alan
dennis roberts wrote:
>
> At 08:46 AM 4/20/01 +1000, Alan McLean wrote:
>
> >So the two good reasons are - that the z test is the basis for the t,
> >and the understanding that knowledge has a very direct value.
> >
> >I hasten to add that 'knowledge' here is always understood to be
> >'assumed knowledge' - as it always is in statistics.
> >
> >My eight cents worth.....
> >
> >Alan
>
> the problem with all these details is that ... the quality of data we get
> and the methods we use to get it ... PALE^2 in comparison to what such
> methods might tell us IF everything were clean
>
> DATA ARE NOT CLEAN!
>
> but, we prefer it seems to emphasize all this minutiae .. rather than spend
> much much more time on formulating clear questions to ask and, designing
> good ways to develop measures and collect good data
>
> every book i have seen so causally says: assume a SRS of n=40 ... when SRS
> are nearly impossible to get
>
> we dust off assumptions (like normality) with the flick of a cigarette ash ...
>
> we pay NO attention to whether some measure we use provides us with
> reliable data ...
>
> the lack of random assignment in even the simplest of experimental designs
> ... seems to cause barely a whimper
>
> we pound statistical significance into the ground when, it has such LIMITED
> application
>
> and the list goes on and on and on
>
> but yet, we get in a tizzy (me too i guess) and fight tooth and nail over
> such silly things as should we start the discussion of hypothesis testing
> for a mean with z or t? WHO CARES? ... the difference is trivial at best
>
> in the overall process of research and gathering data ... the process of
> analysis is the LEAST important aspect of it ... let's face it ... errors
> that are made in papers/articles/research projects are rarely caused by
> faulty analysis applications ... though sure, now and then screw ups do
> happen ...
>
> the biggest (by a light year) problem is bad data ... collected in a bad
> way ... hoping to chase answers to bad questions ... or highly overrated
> and/or unimportant questions
>
> NO analysis will salvage these problems ... and to worry and agonize over z
> or t ... and a hundred other such things is putting too much weight on the
> wrong things
>
> AND ALL IN ONE COURSE TOO! (as some advisors are hoping is all that their
> students will EVER have to take!)
>
> >--
> >Alan McLean ([EMAIL PROTECTED])
> >Department of Econometrics and Business Statistics
> >Monash University, Caulfield Campus, Melbourne
> >Tel: +61 03 9903 2102 Fax: +61 03 9903 2007
> >
> >
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> > http://jse.stat.ncsu.edu/
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>
> ==============================================================
> dennis roberts, penn state university
> educational psychology, 8148632401
> http://roberts.ed.psu.edu/users/droberts/drober~1.htm
>
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--
Alan McLean ([EMAIL PROTECTED])
Department of Econometrics and Business Statistics
Monash University, Caulfield Campus, Melbourne
Tel: +61 03 9903 2102 Fax: +61 03 9903 2007
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Instructions for joining and leaving this list and remarks about
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