It is REALLY easy to screw up a figure, table or number set in a text
if you have no one to review it before submission.  IF you are a peer
reviewer, this is one of the things you probably should be looking at.
 Do the numbers make sense?
Peer review isn't there just to screen out garbage, its also there to
assist authors. This is especially the case when an editor selects a
reviewer specifically because of their exprtise in a particular area.
I recall once as an editor that I sent a paper that involved some
fancy modeling to a mathematical modeler to review the math.  It was
outside of what I did.  She said he didn't know anything about the
biology, and I told her that was easily covered by the other two
reviewers, I just wanted to make sure the math was not tom-foolery.
More of this needs to happen in peer review.  I see a lot of papers
that misuse different techniques.

For example, I recall a paper published in one big ecology journal in
which they used baysian statistics, and misinterpreted the sets.  They
said something had an effect, when the graph and stats clearly
indicated there was no effect!!!  So, the paper ended up widely
covered in the news and people assumed it was what it said, when what
it spent 4-5 pages discussing was complete rubbish.  I've also seen
interval analysis used where fuzzy sets should be used, and the misuse
and over-use of monte carlo analysis is just over the top.

Monte Carlo is only supposed to be used when you have a very great
understanding of the system and very few assumptions and hopefully not
a lot of unpredictable influences.  This is actually not all that
common in ecology and environmental work.  yet, Monte Carlo is used
and abused by simply "Assuming" things are that might not be.  When
you do this with MC you can get VERY wrong answers and there is
virtually no way to check it.  Fuzzy approaches are much more rubust
in this regard as is interval analysis. But, you hardly see anyone who
knows how to use these things, or people are caught 20-30 years
out-of-date thinking they are controversial.

The ideal way to do things is to use fuzzy sets to isolate your data
sets to be used in monte carlo.  That way, you reduce the odds of
going completely off tangent. However, no one seems to do this either.
 it is pretty amazing because outside of ecology, the alternate
methods are widely applied to many different situtaitons.  heck, they
even have fuzzy monte carlo and fuzzy neural networks now.  But, that
is an entire different topic.

The point is, I think it is very reasonable for an editor to select a
peer reviewer form an outside field to check up on methods and
techiques that are outside of his/her expertise, especially if these
are highly technical and particulary novel.  A biologist is not always
the best reviewer for some biology papers in such cases.

On Wed, Apr 30, 2014 at 1:11 PM, David Duffy <[email protected]> wrote:
> "To address this, the publishers of clinical journals must do more to
> ensure that someone takes responsibility for the fact-checking. That could
> involve asking authors to guarantee that they have checked figures, tables,
> text and abstracts for internal consistency. Publishers could require
> authors to make available suitably anonymized data on each patient as
> metadata to the study, so that readers can trace the source of any
> discrepancy that might creep through. Or the publishers could reach into
> their pockets and provide more in-house resources to perform the necessary
> checking. What is not acceptable is for the situation to continue as it is,
> with responsibilities undefined and inexact publishing distorting clinical
> messages."
>
> http://www.nature.com/news/false-positives-1.15119?WT.ec_id=NATURE-20140501
>
> David Duffy
>
> Pacific Cooperative Studies Unit
> Botany
> University of Hawaii
> 3190 Maile Way
> Honolulu Hawaii 96822 USA
> 1-808-956-8218



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