Normal ain't What it Used to Be

Seth Godin made an interesting point: we tend to assume all kinds of data has a “normal distribution”, the famous Bell curve shape, even when it doesn’t. It’s become the default option in most people’s heads: the one thing that sticks from statistics taught at school.

 

The problem, of course, is that lots of things don’t follow a Bell curve. As Steven Strogatz writes in The Joy of x:

“Curiously, these types of distributions are barely mentioned in the elementary statistics textbooks, and when they are, they’re usually trotted out as pathological specimens. It’s outrageous.”

That bias explains why the Bell curve is called the “normal” curve.

 

But the real world is different. A flood or earthquake can cause a huge spike in damage in a particular area, raising the cost to insurance companies. Stock market can have massive moves on a particular day, not a steady movement. The number of deaths in wars follows the same pattern: an outlier can be off the charts (like the World Wars). Epidemics are the same: a black death or Spanish flu or COVID-19 can rip apart any patterns you thought existed for diseases.

 

Relying on the wrong curve to estimate or plan can be deadly, pun intended.

 

In the age of the Internet, it’s easier than ever to collect data and analyse it. The reason we find more and more content meant for niche groups in Amazon and Netflix is because the number of people with esoteric tastes, preferences and needs is not tiny – it’s call the “fat tail” of the curve, to differentiate it from the Bell curve that dies down at the ends. Other common tails include the “long tail” (endless number of items, even if most of them have tiny quantities) and the “heavy tail”. All of which is why Strogatz calls the chapter on all these data distribution curves “The New Normal”. And ends his chapter by saying:

“Fat, heavy and long? Yeah, that’s right. Now who’s normal?”

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