With the increasing speed of information coming at us, how do we know what’s true and what’s not, or even worse – what’s fake?
Figuring out what’s true and false is tough, and then understanding what to do about it can be even tougher. But we should recognize one aspect between lies and the truth.
Lies spread faster. Here’s why.
It’s amazing that we’ve now had our collective awareness heightened to the problem of fake news. I get frustrated at times with the sheer nonsense that seems to swim in the public consciousness, but in search of what I can do about it, I figured I’d share something that happened to me recently.
Science and business seem like two very different disciplines, but is the best approach to learning any different in these two fields? These areas of life seem so unique, and the people in them can be quite varying (one with the nerdy pocket protector and the other dressed in the well-tailored suit). However, both science and business require learning, and the best approach to learning in each is really the same.
The best approach to learning is generally through failure. For example, Thomas Edison failed an astounding number of times
before he invented a working lightbulb, and there are likely thousand of stories about how successes came as a result of many tries and many failures.
Imagine a guy with glasses who used to model baseball stats and play online poker nailing the outcome of the 2012 elections. And when I say “nailing”, I mean that he correctly predicted the U.S. Presidential contest in every one of the 50 states (and nearly every U.S. Senate race, too). He even performed better than some of the most widely-used polling firms. Now imagine that he gives his thoughts on making these types of predictions. That’s exactly what Nate Silver does in his new book The Signal and the Noise" target="_blank">The Signal and the Noise.
I’ve worked in what’s now being called “data science
” for nearly twenty years. The title of Silver’s book – The Signal and the Noise – presents an important and sometimes overlooked part of this science. The “signal” is what we’re looking for in the data, and the “noise” is all the stuff in the data that gets in the way of what we’re looking for.