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Sure.  There are lots of problems with the statistics in scientific papers in many fields.  That's not the point, though.  The point is that you have limited *space* to present your data in a peer-reviewed journal.  I recently did a survey on a professional practice issue in my primary medical specialty as part of a Master's in Public Administration.  For the project, I did a full statistical analysis of 16 of the 50 hypotheses.  The detailed report that I turned into my research advisor was 140 pages long.

When I publish the paper, I will have 5-8 pages to fit my results in.  It doesn't matter whether or not the readers want to see the original data and evaluate the statistics on their own.  They are going to get 5-8 pages of summary results.  You simply can't fit 140 pages of analysis into 6 pages.

The same prinicple applies to imagery.

billo



On Tue, 1 Jul 2008, ian gibbins wrote:

> Search the CONFOCAL archive at
> http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal
>
> I agree, billo, but that does assume that the statistics are done properly.
>
> If we are talking about data integrity and reliable reporting, then a whole 
> squirming bag of worms, leeches and various other unlikable beasts is opened 
> when we bring up the "S" word. As one who does a lot of statistical analysis 
> for folks around here, most people do not understand either the principles or 
> practice of the statistics of data sampling and its subsequent investigation. 
> There are plenty of excellent programs around, as well as a whole raft of 
> texts at various levels, that allow sophisticated data analysis with relative 
> ease these days. Nevertheless, most students, and most researchers, at least 
> in my field, have only rudimentary knowledge.
>
> There have been studies done in the medical literature showing that a very 
> high proportion of papers use the wrong stats, and that many of those papers 
> came to the wrong conclusion as a consequence. I don't know if that applies 
> equally in all fields, and in some journals at least, the standards of 
> statistical analysis are now set pretty high. But there is still an awful lot 
> done badly, if at all... So we get oversamp[ling, undersampling, biased 
> sampling (not Nyqusit, but at the data level), we get inappropriate 
> "normalisation", over-use of percentages, inappropriate use of simple tests 
> (eg t-tests, or oneway ANOVAs) to deal with complex experimental designs, 
> resulting in a tendency to get "significance" when none exists, and then the 
> infamous phrase "... X and Y happened leading to Z, but it did not reach 
> statistical significance ... "
>
>
> Unfortunately, there's plenty more where that came from...
>
> IAN
>
>
>
>
>> 
>> We live in a world in which we have a limited space to demonstrate results. 
>> Most journal articles are there to demonstrate results, not provide 
>> comprehensive data sets. Thus, the appropriate illustration is that which 
>> best demonstrates the finding.  If you have 1000 trials and of these 700 
>> show a result and 300 show nothing, one is not obligated to provide 1000 
>> images, or even 100 or even 10.  It is more useful to provide an 
>> illustration of the positive finding and note the statistics, assuming that 
>> the appearance of the negative finding is intuitively clear (i.e. positive 
>> shows staining and negative is dark).  If the difference between positive 
>> and negative is unclear, it may be useful to provide an image of each, but 
>> I don't think that's usually necessry. I don't see any need to take up 
>> multiple pages of images of negative results just to demonstrate statistics 
>> that are more properly described succinctly in the text.
>> 
>> There is simply no way to provide all of the data in most things in a 
>> couple of pages.
>> 
>> 
>> billo
>> 
>> 
>
> * * * * * * * * * * *
> Prof Ian Gibbins
> Anatomy & Histology
> Flinders University
> GPO Box 2100
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>

billo
http://www.billoblog.com/billoblog