[R] Terminology and canonical statistical user literature

[Lutz Prechelt]

Brian Ripley wrote (to somebody asking about "effect sizes"):
> ...
> Given that, I wonder if you are used to standard terminology.

Good point. But I think for many of us there is more behind that.

I personally belong to an (apparently fairly large) group of
R users who may be enthusiastic, but are statistical laymen
due to a lack of formal education in the area.

The half-knowledge that I have is often sufficient to see that 
many otherwise nice sources of statistical knowledge are 
dangerously incomplete when it comes to explaining the
preconditions required for applying a certain technique
(One example: The extensive NIST handbook at
 http://www.itl.nist.gov/div898/handbook/
 fails to mention that the Wilcoxon rank sum test assumes a
 continuous distribution underlying the sample)
This is not to speak of how to correctly interpret the results.

My situation is this:
- I often have a hard time understanding the R documentation 
due to lack of background.
- I am not in a position to obtain a full background like 
a statistics student would get it.
- I am very interested in carefully checking/validating my 
application of statistical techniques.
- I cannot usually get a consulting statistician to help me.

My question:
Could some of the R gurus maybe agree on a book 
(or very small set of books) with the following properties?:
- explains typical approaches of statistical analysis
  (like MASS, but not as condensed)
- carefully describes preconditions, how to check them,
  robustness if they are violated, interpretation of results
- avoids explaining the innards of the techniques
  (and generally uses the perspective of the computer age)
- uses terminology that is easily mapped to R

If yes, I would be very interested in seeing this list.

I understand that one book cannot cover it all,
but maybe there is at least something like "CAS-"
(Conservative Applied Statistics without S) that
is of this type?  :-)

  Lutz Prechelt

Prof. Dr. Lutz Prechelt;  prechelt at inf.fu-berlin.de
Institut für Informatik; Freie Universität Berlin
Takustr. 9; 14195 Berlin; Germany
+49 30 838 75115; http://www.inf.fu-berlin.de/inst/ag-se/