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One last relevant cross-post before I disappear.
It's tyrannical in its formalism - without even mentioning one is usually learning frequentism. All my lecturers/tutors on the subject had 0 creativity and slaves to method (why they were good at it) and arrogant. People like CS because it allows ones creativity flourish rather than be imprisoned under a pile of rules where there is only one way and one answer. That's a very strange experience you had with statistics- very different from my understanding of how the field works. There should be plenty of creativity involved in stats, as it is much less formal and rigorous than other branches of math. My best understanding of statistics is that it is more mathematically-informed argumentation than anything else, and that statisticians are more like lawyers in some ways than mathematicians (there is an interesting etymology here in the sense that statistics is derived from a root word meaning "state affairs"). The chief difference would be that lawyers argue from man-made laws, while statisticians strive to base their arguments off of natural laws. If lawyers can be creative in their argumentation, then so can statisticians.
Re: Tom | August 26, 2014 at 23:35 One of the reason CS people at my institution are turned off by most statistics classes is R. Yes, it's still better and more engaging than say, STATA or SPSS. But as a programming language it is horrible and needs to die in a fire. Would "CS people" be equally turned off by using bash for system administration scripting? The relationship between R and Statistics and bash/Powershell* and IT can be easily seen to be analogous. Programming languages are more than just syntax, they are platforms that constrain our thought (similar to natural languages- see the Sapir-Whorf hypothesis in linguistics). When you use a general-purpose platform, such as Python, you are not limited in what you can do so you can easily write distracted code that falls outside of the paradigm you are working within- this can be a blessing for a creativity, but a curse for fields that require the use of a common internally consistent framework, such as statistics. Domain-specific languages, while certainly atrocious in their design (don't get me started on MATLAB), at least make sure that analyses are reasonably reproducible and homogeneous, since everyone must use the same limited subset of functions (and by "everyone" I mean the community of users for a given language, which is also crucial). It is my conviction that both domain-specific and general purpose languages have their place- general languages like Python can be for more idiosyncratic, creative configurations and platforms like bash and R can be for more stereotyped tasks. Sometimes you only need a solid and well-developed pair of scissors, not a Swiss Army Knife with a functional, but lower quality pair of scissors. For mature fields, tools that do one thing and do it well are essential. (* Yes I know, Perl/Python are also used for some system administration tasks, but I am assuming that those tasks are substantially more idiosyncratic than basic init scripts and the like. I am open to being proven wrong on this point.)
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Aug 27, 2014