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I have personally worked in SAS, SPSS and R, and while I agree there are advantages to SAS for example, R has a definite place as free (open source) software with lots of tried and true modules to enable, and easy to access virtually free training via LinkedIn Learning (or Fka Lynda.com). Many small enterprises cannot afford SAS, or the cost of its training, and many large enterprises are trying to force the transition to open source solutions to save money. I have found R extremely easy to learn, and I love some of the graphics details - such as the color spectrums available. It is certainly better for folks with only occasional use than some other options.

Maybe the overload mentioned at the start is simply an indication that the theory needs to be taught first. In my educational experience, I learned stats as math, complete with proofs required, and software was learned separately as a tool for for applying the theory. I would not be surprised if understanding ANCOVA, for example, uses different patterns of thought than coding running one.

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Comment by Rick Henderson on June 1, 2017 at 9:54am

You've made some very good points Mary. I think a lot of it depends on a person's role in their organization. Some may need to know theory of ANOVA so deeply they could code it in their sleep. Some analysts will just have to know when to recognize the software output is correct.

I think it can be a fair statement that not all statisticians can write code. Every tool has its place.

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