The way you frame it makes it sound like the author was saying Julia is a less trustworthy language, which isn't what they're saying at all. Here's the quote in context:

> Still, if you are new to (Julia) programming and statistics then most likely you should calibrate your tools first. Before you run some statistical analysis you may want to try it out on an example from a textbook written by an expert (not me though) and see if you get the same (or at least comparable) result on your own. Although this is a sound approach, I suspect you are more prone to visit some statistical blog or internet forum and go with the examples that are contained there. One such option is rseek.org, i.e. a search engine for the R programming language.

> ... Once I got both outputs that are similar enough I can be fairly sure I did right. Otherwise I should investigate where the differences come from and possibly make some necessary adjustments.

It doesn't say to compare it against R, it says that if you are new to programming and statistics you should check your work against known-good answers or against a second implementation before rushing ahead, and it gives R as an example of a place to find code that you can use to check your work.

This isn't advice about one language being better, it's the usual advice to solve the same problem in two different ways to make sure you got it right!

That is a good practice for any statistical software you are using. You should validate your R code with Python or Julia.

Isn't it always a good idea to test your implementations against a known correct implementation? Like, if I were testing, say, a SHA implementation, I would also test against the results from an independent implementation. How is this an issue with Julia the language?

I validate my C++ code by comparing against a Python implementation

this is just a normal sanity check and is not a strike against Julia (or R)

"I suppose SAS is too expensive, so R will have to do"?