Tag: statistics is awesome


Statistics is not math...

Statistics depends on math, like a lot of other disciplines (physics, engineering, chemistry, computer science). But just like those other disciplines, statistics is not math; math is just a tool used to solve statistical problems. Unlike those other disciplines, statistics gets lumped in with math in headlines. Whenever people use statistical analysis to solve an interesting problem, the headline reads:

“Math can be used to solve amazing problem X”


“The Math of Y” 

Here are some examples:

The Mathematics of Lego - Using data on legos to estimate a distribution

The Mathematics of War - Using data on conflicts to estimate a distribution

Usain Bolt can run faster with maths (Tweet) - Turns out they analyzed data on start times to come to the conclusion

The Mathematics of Beauty - Analysis of data relating dating profile responses and photo attractiveness

These are just a few off of the top of my head, but I regularly see headlines like this. I think there are a couple reasons for math being grouped with statistics: (1) many of the founders of statistics were mathematicians first (but not all of them) (2) many statisticians still identify themselves as mathematicians, and (3) in some cases statistics and statisticians define themselves pretty narrowly. 

With respect to (3), consider the following list of disciplines:

  1. Biostatistics
  2. Data science
  3. Machine learning
  4. Natural language processing
  5. Signal processing
  6. Business analytics
  7. Econometrics
  8. Text mining
  9. Social science statistics
  10. Process control

All of these disciplines could easily be classified as “applied statistics”. But how many folks in each of those disciplines would classify themselves as statisticians? More importantly, how many would be claimed by statisticians? 


Is Statistics too darn hard?

In this NY Times article, Christopher Drew points out that many students planning engineering and science majors end up switching to other subjects or fail to get any degree. He argues that this is partly due todo the difficulty of classes. In a previous post we lamented the anemic growth in math and statistics majors in comparison to other majors. I do not think we should make our classes easier just to keep these students. But we can certainly do a better job of motivating the material and teaching it more interesting. After having fun in high school science classes, students entering college are faced with the reality that the first college science classes can be abstract and technical. But in Statistics we certainly can be teaching the practical aspects first. Learning the abstractions is so much easier and enjoyable when you understand the practical problem behind the math. And in Statistics there is always a practical aspect behind the math. The statistics class I took in college was so dry and removed from reality that I can see why it would turn students away from the subject. So, if you are teaching undergrad (or grads) I highly recommend the Stat labs text book by Deb Nolan and Terry Speed that teaches Mathematical Statistics through applications. If you know of other good books please post in the comments? Also, if you know of similar books for other science, technology, engineering, and math (STEM) subjects please share as well.

Related Pots: Jeff on “The 5 most critical statistical concepts”, Rafa on “The future of graduate education”, Jeff on “Graduate student data analysis inspired by a high-school teacher