Repost: The 5 Most Critical Statistical Concepts03 Jul 2013
(Editor’s Note: This is an old post but a good one from Jeff.)
It seems like everywhere we look, data is being generated - from politics, to biology, to publishing, to social networks. There are also diverse new computational tools, like GPGPU and cloud computing, that expand the statistical toolbox. Statistical theory is more advanced than its ever been, with exciting work in a range of areas.
With all the excitement going on around statistics, there is also increasing diversity. It is increasingly hard to define “statistician” since the definition ranges from very mathematical to very applied. An obvious question is: what are the most critical skills needed by statisticians?
So just for fun, I made up my list of the top 5 most critical skills for a statistician by my own definition. They are by necessity very general (I only gave myself 5).
- 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.
- A knowledge of exploratory data analysis - how to make plots, how to discover patterns with visualizations, how to explore assumptions
- Scientific/contextual knowledge - at least enough to be able to abstract and formulate problems. This is what separates statisticians from mathematicians.
- Skills to distinguish true from false patterns - whether with p-values, posterior probabilities, meaningful summary statistics, cross-validation or any other means.
- 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.
What are your top 5? What order would you rank them in? Even though these are so general, I almost threw regression in there because of how often it pops up in various forms.