All in the Numbers

“Take nothing on its looks; take everything on evidence. There’s no better rule.”
-Charles Dickens, Great Expectations

 
I wasn’t looking for a literary quote that went along with the two science books I recently finished, but I found it all the same. Like suddenly seeing a newly learned word everywhere you look, I’m finding quotes and reasons to follow the advice I read in Nate Silver’s The Signal and the Noise and Richard Dawkins’s Unweaving the Rainbow.

I enjoy science. I always have. I wanted to be an astrophysicist until higher math came along, and proofs and differential equations refused to make sense, no matter how often they were explained. My career options changed a bit after that, but I never lost my interest in science. I don’t find it necessary to understand the math to be intrigued by Schrodinger’s Cat or be amazed by the fact that we simple little humans have built things that allow us to look back billions of years to the beginning of the universe.

In Unweaving the Rainbow, Dawkins unravels the idea that science sucks the life out of wonderful things, as though knowing that a rainbow is caused by millions of raindrops breaking apart sunlight as they fall to the ground somehow subtracts from its beauty*, or figuring out why a flower evolved to the exact shape and coloration to attract the right kind of insect to spread its pollen makes a tulip any less delightful after a long winter. Dawkins argues that a knowledge of why the universe works the way it does can deepen one’s appreciation for it, and that being able to step back, think critically and realize that things like horoscopes and TV psychics are just tricks that are easy to see through once you look behind the scenes. And while he doesn’t have the easy wit of Carl Sagan (who does?), he is capable of making his point and backing it up in a rational manner.

Nate Silver’s The Signal and the Noise covers vastly different territory- statistics, probability, and the ability of humans and computers to predict the future. Silver describes how the vast amounts of data we create every day, high processor speeds, and advances in human knowledge have allowed us to be able to predict things like the weather and a baseball team’s prospects with far more accuracy than we could thirty years ago, but we still can’t predict an earthquake or an economic forecast. And we probably won’t be able to for a long, long time. For all that we are a clever species, some models simply require more data than even our best computers can work with, and other events- like earthquakes- are still too much of a mystery for us to properly predict.**

Scientists are often accused of being arrogant in their knowledge, and yet a good scientist is the most uncertain person of all- someone who knows the limits of her knowledge, knows that she will never find all the answers, that people who don’t know of those limitations will mock her for voicing her uncertainty- even though she knows that her own findings could be overturned at any time by future findings. And yet she carries on, because that itch we call curiosity must be scratched.

And that’s part of what’s remarkable about good science writing, the acknowledgement that, however fascinating a new discovery or theory is, it could be overturned at any time. I think it takes nerves of steel for a team of scientists to take on Einstein’s Theory of Relativity, to put it to the test with the newest instruments to see if the calculations hold up, but people are doing it all the time (and winning Nobel Prizes in the doing).

So how does this relate to a non-scientist? It’s pretty simple. What Dawkins and Silver (among many others) advise us to do is to step back, take a breath, and consider our own biases when we look at the world around us, to ask ourselves if its merely our own fears that hold us back, or if there is a rational reason for our misgivings. Am I not talking to that guy over there because I know he’s boring or a bigot or somesuch, or do I refuse his conversation because he’s really tall or he has weird blue hair? Am I missing a good conversation based on the fact that I don’t like blue hair, and should I put that bias in its place and see beyond that unnaturally color?

That’s a silly example, I know, but it’s no more ridiculous than many of the biases we cling to for fear of change (or whatever). The world would be a happier place if we investigated our own reasonings and discarded the ones that led us into the darkness.

 

 

 

* A rainbow is created when a raindrop breaks up a wave of light into its constituent wavelengths and projects a specific wavelength onto the clouds behind it. Since different colors have different wavelengths and those wavelengths are projected at different angles, a single drop projects the entire spectrum as it falls from the sky. Ergo, a single rainbow is caused by a storm’s worth of raindrops individually breaking apart light itself to form a solitary band of colors. Pretty cool, huh?

** In 2012, an Italian court catapulted itself back to the sixteenth century by convicting six scientists and a government official of manslaughter for failing to predict the unpredictable. No matter what method geologists have used to try to predict earthquakes, not a single method has worked reliably. It’s easy enough to say that a city like San Francisco will likely experience a major event in the next century, but it’s impossible to predict that one will happen in a specific year or day.

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