I haven't read the articles. It sounds suspicious to me, especially if
applied to social science.
I recently came across an example where fitting a normal distribution to a
time series (ie ignoring time order) was taken as evidence that the data
were "random".
If a distribution does not fit that might suggest that the theoretical
model giving rise to the distribution might not hold (or it might be that
there are extra complications, for example observational error).
If the distribution does fit that means very little ... if there is good
theoretical justification for the model then finding it "fits" could be
reassuring.
Jane Galbraith
> After it was covered in New Scientist, I've been struggling to
understand
> the principles behind an article in Nature (C Venditti et al,
Phylogenies
> reveal new interpretation of speciation and the Red Queen, Nature 463,
349-352 (21 January 2010). As New Scientist explained it, Pagel and
others had been trying to track evolutionary histories by looking at the
fit between evidence and four statistical distributions (exponential,
variable rates, lognormal and normal). Looking at the paper, it's
rather
> more complex: the fit is being determined by Bayesian methods, five
distributions are reviewed rather than four, and the second-best
performing distribution is a Weibull distribution.
>
> Can anyone point me to some illustrations of work in social science
> which has approached the problem in the same way - fitting
distributions,
> not as a description of patterns, but as a method of identifying causal
influences?
>
> I've seen the Weibull distribution used in some work to describe the
likelihood of escaping from poverty, but I'd assumed this was
descriptive,
> and I'm now having to reexamine that assumption. What are the
theoretical
> implications of assuming a Weibull distribution? Is there any guide to
its application in social science that someone can point me to?
>
> Paul Spicker
>
>
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