I got into a conversation on Twitter (find me there as @peterflomstat) about the user-friendliness of statistical software. I have heard R described (appropriately, I think) as being “expert friendly”. This led to a conversation about whether and when that is good or bad. But we agreed that it would be hard to discuss that in 140 character blocks.
Many statistical procedures are complex. Even ones that are often regarded as relatively simple (e.g. ordinary least squares regression) have some oddities and make assumptions and so on. If statistical software is so easy to use that anyone can simply point-and-click and get results, that can easily lead to really silly statistics being done. If there is some form of review by someone who knows statistics then this silliness may be caught (or it may not). But sometimes there is no such review. If software is a little harder to use, then perhaps some of this can be avoided. “I did multiple regression” “Did you check for outliers?” “Huh?” is not a good conversation.
Of course, it is also possible to do really silly things with other statistical software. But there is a little bit of a bar to entry.
But I think that “user-friendliness” really has two aspects:
- How much do you have to know about statistics in order to use the software?
- How much do you have to know about programming in order to use the software?
My view is that a fairly high requirement on the first of these is a good thing, while a high requirement on the second is not. Many statisticians are good at programming, but that should not be a prerequisite.
Specialties: Regression, logistic regression, cluster analysis, statistical graphics, quantile regression.