## Friday, July 5, 2013

### Critical Statistical Skills

The gents over at Simply Statistics re-posted what they consider the five most critical concepts/skills every statistician should possess and although this list could probably be debated ad infinitum, I think their list is a solid start.  (I've also wondered what constitutes a competent and qualified statistician/data analyst versus a merely adequate one but I never codified the traits with a list.)  Their list, admittedly general, and limited to five, is thus (pasted verbatim from their post):
1. The ability to manipulate/organize/work with data on computers - whether it is with excel, R, SAS, or Stata, to be a statistician you have to be able to work with data.
2. A knowledge of exploratory data analysis - how to make plots, how to discover patterns with visualizations, how to explore assumptions
3. Scientific/contextual knowledge - at least enough to be able to abstract and formulate problems. This is what separates statisticians from mathematicians.
4. Skills to distinguish true from false patterns - whether with p-values, posterior probabilities, meaningful summary statistics, cross-validation or any other means.
5. The ability to communicate results to people without math skills - a key component of being a statistician is knowing how to explain math/plots/analyses.
I heartily agree with #1 although I would consider any statistician that routinely uses MS Excel for their analyses and graphics as either lazy or marginally incompetent.  Numbers two and four are also important and I think the longer you practice statistics and the more problems you encounter, the better you get at these skills.  Number three could be distilled down even further into theoretical versus applied statisticians where the theoretical statistician toils away in academia teaching graduate-level mathematical statistics and the applied statistician engages in dirty data collection, cleanses that data, then outputs descriptive and inferential statistics.  The last point, communication, is a skill that is often given short shrift but is one, as the folks at Simply Statistics also agree with, shouldn't be overlooked.

It's easy to lose sight of the broad skill set a statistician must possess to remain effective and competent, especially as one specializes, so it's nice to be occasionally reminded.