[R] Re: Thanks Frank, setting graph parameters, and why social scientists don't use R
john.maindonald at anu.edu.au
Wed Aug 18 12:57:22 CEST 2004
There are answers that could and should be applied in specific
situations. At least in academia and in substantial research teams,
statisticians ought to have a prominent part in many of the research
teams. Senior statisticians should have a prominent role in deciding
the teams to which this applies. why should it be ok to do combine
high levels of chemical expertise with truly appalling statistical
misunderstandings, to the extent that the suppose chemical insights are
not what they appear to be?
There should be a major focus on training application area students on
training them to understand important ideas, to recognize when they are
out of their depth, and to work with statisticians.
There should be much more use of statisticians in the refereeing of
published papers. Editors need to seek advice from experienced
statisticians (some do) on what sorts of papers are candidates for
Publication in an archive of the data that have been used for a paper
could be a huge help, so that others can check whether the data really
do support the conclusion. Even better, as Robert Gentleman has
argued, would/will be papers that can be processed through Sweave or
Really enlightened people (in the statistical sense) in the applied
communities will latch onto R, as some are doing, because the
limitations inherent in much other software so often lead to crippled
and/or misleading analyses. Increasingly, we can hope that it will
become difficult for statistics to in various applied area communities
to proceed on its merry way, ignorant of or ignoring most of what has
happened in the mainstream statistical community in the past 20 years.
The statistical community needs to be a lot more aggressive in
demanding adequate standards of data analysis in applied areas, at the
same time suggesting ways in which it can work with application area
people to improve standards.
It is also fair to comment that the situation is very uneven. There
are some areas where the standards are pretty reasonable, at least for
the types of problems that typically come up in those areas.
John Maindonald email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Bioinformation Science, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
On 18 Aug 2004, Bert Gunter wrote:
So we see fairly frequently indications
of misunderstanding and confusion in using R. But the problem isn't R
-- it's that users don't know enough statistics.
. . . .
I wish I could say I had an answer for this, but I don't have a clue. I
do not thing it's fair to expect a mechnical engineer or psychologist
or biologist to have the numerous math and statistical courses and
experience in their training that would provide the base they need. For
one thing, they don't have the time in their studies for this; for
another, they may not have the background or interest -- they are,
after all, mechanical engineers or biologists, not statisticians.
Unfortunately, they could do their jobs as engineers and scientists a
lot better if they did know more
statistics. To me, it's a fundamental conundrum, and no one is to
blame. It's just the reality, but it is the source for all kinds of
frustrations on both sides of the statistical divide, which both you
and Roger expressed in your own ways.
. . . .
More information about the R-help