The R user point-of-view about “Statistics Without the Agonizing Pain”

(This article was first published on  MilanoR, and kindly contributed toR-bloggers)     

Contrary to general expectations, or at least to my expectations, the logical and analytical concepts behind statistics are rather difficult to understand by engineers. In general, despite their heavy background in maths and their above average fluency with computer programming, there seems to be a broken bridge with the statistical world and often they prefer to stay in the safe "math bank" of the river, despite the road to statistical fluency is shorter than they think.

For example, I noticed the aforementioned issue as I heard engineers referring (inappropriately) to the Law of Large Numbers. Most of the time they were just talking of the so called "Gambler’s fallacy" without having idea of neither of the two.

Worse than the LLN, the t-test and the sampling distribution appear as inscrutable concepts and too heavily affected by a “stochastic mechanisms”, too far away from the rational deterministic approach of engineering. I had definitely confirmation of my thoughts when scrolling through R bloggers in a rainy Saturday afternoon I stumbled in the following video:https://www.youtube.com/watch?v=5Dnw46eC-0o&feature=player_embedded

In this video, for the first time I saw the essence of R: a collaborative effort that puts together a huge amount of different knowledge and returns back something to everyone. If engineers can program a computer (as they can indeed), they could have direct access to the deepest, most fundamental ideas in statistics (John Rauser – Data Scientist at Pinterest). And this is true in general, for anyone able to/willing to program a computer.

As R requires at the very end of its processes micro-simple-operations, the programming/computational approach it allows to carry out, permits to completely unfold the statistical process behind the complex formulae, so that everyone can understand the deepest meaning of the statistical science.
And at the end of the day, what is really important for R and its community is that the collaborative effort behind the development of the software "make the complicated simple", so that an always bigger audience could understand its power and the science behind.