Why statistics?
This a personal post on what I think statistics is, why I was drawn to study it, and why it’s basically a super awesome cool subject everyone should know more about.
Many people appear to regard the subject with suspicion, feeling that statistics are more often used to bamboozle than to enlighten.
When, in my 20s, I started studying Applied Statistics in the evenings I got a lot of puzzled looks from friends and colleagues. What on earth did I want to do that for? What was the point?
To me this seemed odd.
As a journalist you are always trying to find out what is going on, and what it might mean. In my History degree I looked to try to find out what went on in the past and what it might mean.
These questions are very similar to those that statisticians ask. Only, statisticians have an additional tool to help them — and it’s a big one: maths.
Mathematical frameworks help us understand what conclusions we can draw from data, and how confident we can be in them. They give us tools to deal intelligently with what we do not — or cannot — know.
And this is great. Because of course, in the real world, we never get all the information we need. We are always having to piece together a picture based on what we can see, and we need steer on what we cannot see and how important that might be.
For example: I might have a disagreement with my dog, Markus, about his biscuits. He might insist that Brand A’s dog biscuits are bigger than Brand B’s, and therefore he should be bought Brand A’s.
As he’s a scientifically minded dog, he would allow me to take a random sample of each to weigh.
What if, based on the sample, Brand A’s biscuits are slightly larger than Brand B’s? Are Brand A’s biscuits bigger, or could this be a fluke?
Well, there are statistical tests to decide whether there is actually enough evidence to accept Markus’s claim.
There are also well defined frameworks to assess the probability that you will wrongly accept the claim by chance (that is, you select a random sample that happens to be of unusually large biscuits from Brand A, when in fact Brand A’s biscuits are not bigger than Brand B’s).
Of course, advanced statistical methods deal with much more difficult and nuanced situations than my dog biscuit example.
Statistics has not advanced to the point where we can guarantee that we are right about everything all the time*. But that’s not a reason to dismiss it.
Statistical methods, if used properly, bring us a vast amount of insight into problems. Why wouldn’t you want to know about that?
*On the subject of things I’ve not always been right about: I was going to put something in this post about Benjamin Disraeli’s famous “Lies, damned lies and statistics” quote, only to discover that we don’t actually know who coined that one.[1]
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