Tag Archives: defining science

John Snow and the cholera outbreak of 1854: Revealing an unintuitive truth using data

Truth reveals itself to us in many different ways. Sometimes, it takes the form of an amazing revelation, an eye-catching explosion of color, or a terrifying act of nature. Other times, it takes on a more subtle form, discovered only through a combination of patience, knowledge, and determination.

Turn the clock back a few hundred years, and you would find a culture that did not have the sophisticated data analysis techniques to uncover the truths of natural world that we have today.  Claims were often backed up by “common sense”.  Society lacked a way of quantifying information and letting the data speak for itself. But this mentality began to change in the 1800s, marking an important shift in our scientific culture that continues to this day.  While the process spanned several generations and countless individuals, one of the more interesting stories is that of a man named John Snow.
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You can’t spell “prescience” without “science”

On April 6, 2009, there was an earthquake in L’Aquila, Italy; 308 people were killed. Six days before that, on March 31, there had been a meeting of the Commissione Grandi Rischi (“Major Risks committee”) in L’Aquila, which concluded that a major quake was unlikely. Members of the committee were indicted the following year on manslaughter charges for not warning the citizens of L’Aquila to evacuate. This story contains an important lesson for any scientist, or at least some important food for thought. What exactly is our responsibility to society, when it comes to predicting the future?

Why empiricism will always be imperfect

A new year is upon us, and that’s always a great time to clean out the skeletons in your closet. So without further ado, let’s take a look at Jonah Lehrer’s explanation of “the decline effect” (published in The New Yorker last month). Lehrer describes this odd phenomenon whereby statistical significance of previous scientific findings seems to decrease with age, as we get further and further away from the time that it was initially reported in literature.

As any scientist can tell you, the holy grail of an experiment is a low p-value, a statistical measure that tells whether your findings are indicative of an actual effect, not just randomness and chance. This sounds fairly straightforward – of course we want to find things of actual importance, rather than being lulled into a false discovery by arbitrary data – but it turns out to be much hazier than a simple “yes” or “no.”
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