I love thinking about numbers, and so there are few things more important to me than a proper appreciation of numbers and looking at them with an appropriate perspective. Scale can be hard to fathom. Especially when the numbers are very big or very small. Continue reading
Here be dragons: the hidden dangers of suggestive correlations
I know that we both agree on this point: correlation does not mean causation. It’s an adage easy to remind ourselves whenever we see spurious correlations (as this pretty awesome site demonstrates).
But how about suggestive correlations? Those times when we can make a narrative about our data, when we can effortlessly turn that correlation into a causation?
Well, that’s a completely different ball game.
Correlation versus causation? I prefer correlation and causation
One of my mom’s favorite sayings about the perils of using only correlations to infer causation is “breakfast doesn’t cause lunch.” And I have to agree with her: this statement is a perfect illustration of the real danger in taking a correlation to mean causation.
Does Marijuana’s Gateway-Drug Status Argue Against Its Legalization?
I always find it astonishing how bad we are, generally, at if-then statements. Even scientists make blunders with logic fundamentals—usually in life outside the lab, when we’re not paying close attention to those common pitfalls.
Understanding (and respecting) the limits of your data
The business of gathering data, of performing experiments—this is the bread and butter of being a scientist. The task of doing controls, of analyzing data—these are the essential jobs that just must be done if we want to make models and to understand the universe. Our ability to dissect the truth relies entirely upon the quality of the data we generate.
Unicorns, MBAs and Background Distributions
Numbers are vital to science and scientists. No matter how obvious a hypothesis may seem, just thinking that something is true doesn’t make it so. But equally important is ensuring that we analyze data appropriately, carefully and rigorously. Scientific pitfalls can lurk in even the seemingly simplest of analyses, and a good scientist is always her harshest critic.