There’s a certain liberation in being able to constantly update your beliefs based on the evidence.
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Is the presentation of the data hiding something? Was anything excluded or minimized? Was anything glossed over? Was one part of the graph highlighted or emphasized to draw attention away from something else?
The most interesting stories are when theory connects to observations, when there's a strong attempt to refute or bolster some piece of (un)known science. And here the name of the game is error bars.
I've had a pithy saying that I like to toss around. I don't know where I got it, and I can't find a source for it, so I'll go ahead and take credit for it: the first thing that lies to you is the data. Never, ever trust it.
You've probably encountered the phrase "extraordinary claims require extraordinary evidence." Well, it's not just some pithy saying that relieves you of the burden of having to believe every random statement you might encounter in your life.
Last week I mentioned an odd term, p-value, which is commonly used in deciding whether your results are worth mentioning to your colleagues and the public. Of course it has a strict and narrow meaning, and of course that meaning is abused and misinterpreted in discussions about science.
Science is hard. Scientists have to stare at mountains of data and try to figure out what secrets nature is whispering to them. There are innumerable blind alleys, dead ends, and false starts in academic research. That's life, and that's why over the centuries we've developed sophisticated statistical techniques to help lead us to understanding. But if you're not careful, you can fool yourself into thinking there's a signal when really you've found nothing but noise.
Once again there's a fresh round of blaring headlines and nervous chatter about potential links between cell phone use and cancer, this time based on a recent study purporting to show that a group of rats exposed to radio waves had greater incidents of tumors compared to a control group.