Reblog: the statistics software signal

January 12, 2013

(This article was originally published at Learning From Data » Statistics, and syndicated at StatsBlogs.)

Some interesting comments about statistical softwares and the people who use them from Sean J. Taylor‘s The Statistics Software Signal

What your statistical software says about you (to me):

  • R: You are willing to invest in learning something difficult.  You do not care about aesthetics, only availability of packages and getting results quickly.
  • Python or JVM languages: You are a hacker who may have already been a programmer before you delved into statistics. You are probably willing to run alpha or beta-quality algorithms because the statistical package ecosystem is still evolving. You care about integrating your statistics code into a production codebase.
  • Julia: You are John Myles White.
  • Stata: You are an economist who doesn’t care to code your own estimators, probably because your comparative advantage lies elsewhere.  Possibly you are doing sophisticated work with panel data where Stata is the only game in town.  You don’t care that you can’t do proper programming because you’re not a programmer.
  • SPSS: You love using your mouse and discovering options using menus. You are nervous about writing code and probably manage your data in Microsoft Excel.
  • Matlab: You definitely know what you’re doing and you care about performance. You know Matlab is expensive but you aren’t the one paying for it. You live in a bubble where everyone you know uses Matlab.
  • Mathematica: You are an aesthete who believes everything Stephen Wolfram says.
  • SAS: You are an analyst for a large pharmaceutical company, and SAS is all you have ever known. You have a large library of custom SAS macros, so that (clearly) makes you a programmer. That anyone would want to hand-code statistical methods leaves you utterly baffled. If SAS does not ship with a particular statistical method, then it probably isn’t important. (h/t Chris Fonnesbeck)

Seems like there are some truth in them. How about we use these as some kind of prior distribution next time.

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