As Carl said, a learning curve plots performance (knowledge) as a
function of time, at least that is how it has been used in psychology
and the learning sciences for a hundred years. So a "steep" learning
curve implies "easy to learn" .... but the metaphor of being "steep" =
"hard to climb" = "difficult" seems to be intuitively compelling for
many people, so I gave up on correcting people on the wrong use of
"steep learning curve" ;-)
Rüdiger Pfister
Message: 2
Date: Sat, 11 Dec 2010 10:25:50 +1300
From: Rolf Turner<r.tur...@auckland.ac.nz>
To: Carl Witthoft<c...@witthoft.com>
Cc: "r-sig-mac@r-project.org"<r-sig-mac@r-project.org>
Subject: Re: [R-SIG-Mac] learning R
Message-ID:<e406b41a-f113-4907-b385-2170ac66c...@auckland.ac.nz>
Content-Type: text/plain; charset="us-ascii"
I agree with you completely about ``begging the question''. The
nearly universal misuse of this expression drives me crazy. I'm
not so sure about ``steep learning curve'' however. My impression
is that this phrase has *always* been used to convey the idea that
a subject area is difficult to learn, whence to use it (as you suggest)
in the sense that the subject area can be learned quickly would be to
change the original meaning of the phrase. That would be undesirable,
even given that the original meaning is counter-intuitive.
I recall having heard/read a ``justification'' for the original meaning
to the effect that what is envisaged is plotting effort expended on
the *y* axis and knowledge level on the *x* axis. Thus a steep learning
curve would entail expending a great deal of effort for a small increase
in knowledge.
I agree that this is a silly choice of axes --- I certainly wouldn't make
such a choice. But I don't suppose that there's any law against it.
cheers,
Rolf Turner
On 11/12/2010, at 4:22 AM, Carl Witthoft wrote:
Next to "begging the question," the phrase "steep learning curve" is
probably the most misused cliche out there.
A 'learning curve' represents knowledge (or understanding) as a function
of time. THerefore, the steeper the better.
Please help save the English language from descent into Humpty-Dumpty
land, and train your colleagues in the correct usage of both these terms.
Carl
Message: 2 Date: Thu, 9 Dec 2010 09:51:27 -0800 From: Payam
Minoofar<payam.minoo...@meissner.com> To:
"r-sig-mac@r-project.org"<r-sig-mac@r-project.org> Cc:
"deniz.kellecio...@gmail.com"<deniz.kellecio...@gmail.com> Subject:
[R-SIG-Mac] R for Mac, good enough?
Message-ID:<53df393b-2037-4b0d-890f-8dbaa1ba1...@meissner.com>
Content-Type: text/plain; charset="us-ascii"
The power of R is virtually unmatched, and R for Mac works extremely
well.
The learning curve is steep, however, and documentation is difficult
to grasp, even though it is abundantly available. I am more partial
to a commercial data analysis package with which I grew up, but I
have done enough work with R on the mac platform to recommend it
highly.
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End of R-SIG-Mac Digest, Vol 94, Issue 6
****************************************
--
Hans-Ruediger Pfister
Professor of Psychological Decision Research and Methods
Institute of Experimental Industrial Psychology (LueneLab)
Leuphana University Lueneburg
D-21335 Lueneburg / Germany
http://www.leuphana.de/en/hans-ruediger-pfister.html
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