[EMAIL PROTECTED] (Robert Dodier) wrote in message news:<[EMAIL PROTECTED]>...

Robert, Good points: I comment below.

> [EMAIL PROTECTED] (dave martin) wrote:
> 
> > Fisher's F-test can be used to quantitatively compare
> > two models of data.
> 
> I assume that you mean here to treat the ratio of mean square errors
> as the test statistic for an F test. I'm not seeing the justification
> for this: it is assumed in an ordinary F test that the numerator and
> denominator are independent; this is almost certainly not the case 
> when comparing errors made by two models of the same data.

Is this objection satisfied by using two sets of experimental data;
e.g. conducting the same experiment twice?

Also (shamelessly showing my naivety) why aren't the deviations from
two independent models independent when the same set of data is
applied?

> 
> More fundamentally, an F test, or any significance test, cannot tell
> you what you really want to know. More on this below.
> 
> > The F-test can answer the question "Are these two models
> > significantly different at the X% level?".
> 
> This is an interesting question, but it certainly is not the
> question that is answered by a significance test. 
> 
> You know at the outset that the two models are different, so
> you will almost certainly get a rejection of the null hypothesis
> with a large enough data set (assuming that you can correctly
> compute the distribution of the test statistic). So a significance
> test mostly tells you whether or not you have a large data set.
> This is not very interesting.

Actually it is VERY interesting when an author states that her/his
data demonstrates that model A is correct to the implicit (or even
explicit) exclusion of model B. This is often the case in my field
(Materials Science), and I suspect in many physical science areas.

> 
> All the standard statistics books say that statistical 
> significance is not the same as practical significance. 
> True enough, but why bother, then, with hundreds of pages
> of statistical significance when you know it's not 
> what you really want?
> 
> For what it's worth,
> Robert Dodier
.
.
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