Thank you all for the response. If you allow me, I will try to get the context in which I posed the questions into a sharper focus, and propose a conclusion.

Since I signed up for a password here at PM, I have learned a lot by reading, and reading more and trying to follow your discussions. Most of this in my spare time, since I am not supposed to programming, I’m supposed to be analyzing data. I enjoy learning Perl to a great extent, and there are many pitfalls that I am now able to avoid, thanks to PM.

However, I started this thread with a quote from Larry Wall, because to be perfectly honest, in a sense I was fooled by the Perl evangelists into believing that this would be a language that would really allow me to define a subset of instructions that I needed for the (relatively) simple job at hand, and think no more of it. I work at a university, so any new tool or technique I develop, I’m supposed to be able to explain to someone else (be it a member of staff or a student). If I quote Larry, some of them will be as enthusiastic as I am, but if I tell them that ‘IT=learning perpetually’ it is unlikely that they will rush out and buy a copy of the Camel. Science is learning perpetually too, and most of us hardly have the time to keep up with the primary literature. If there is no ‘copy and pasteable’ set of instructions I can come up with, or a small and well defined subset of the language, then the applicability of this in a course on statistics (for example) is rather limited.

Permutation analysis appears to be a promising (or at least a very useful) tool in the biological sciences 1,2. It has the great disadvantage that one has to be computer savvy and know some basic programming to be able to apply them effectively. From your response so far, instead of talking about the marvels of this language (as I have done in public), I should advise them to either hire me (which is not that bad an option, since I will reach the end of my contract soon :) or seek other, professional, advice when staff or students want to apply permutation methods or need to massage large datasets.

Refs:
1 Manly, B. F. J. (1991). Randomization and monte carlo methods in biology. New York, Chapman and Hall.
2 Good, P. (1994). Permutation tests, a practical guide to resampling methods for testing hypotheses. New York, Springer Verlag.