[Cross-posted at Signal/Noise]
In keeping with Patrick’s theme on methodological discussions, I thought I would cross-post this recent piece from my personal blog.
Nathan at Flowing Data puts words to an idea I’ve had for a while, but could never figure out how to communicate. He writes, “[T]he most important things I’ve learned [in statistics courses] are less formal, but have proven extremely useful when working/playing with data.” Some of the lessons learned include:
- [T]rends and patterns are important, but so are outliers, missing data points, and inconsistencies.
- [I]t’s important not to get too caught up with individual data points or a tiny section in a really big dataset.
- [D]on’t let your preconceived ideas influence the results.
- The more you know about how the data was collected, where it came from, when it happened, and what was going on at the time, the more informative your results and the more confident you can be about your findings.
- [A]lways ask why. When you see a blip in a graph, you should wonder why it’s there. If you find some correlation, you should think about whether or not it makes any sense. If it does make sense, then cool, but if not, dig deeper.
The point is that regardless of whether you are formally trained in and choose to leverage sophisticated statistical methods, there is a great deal to be gained by thinking like a statistician. I would actually go farther here and say that the statistician part is somewhat besides the point. Thinking like a methodologist is the key.
I would agree with Nathan that the most translatable skills that I learned in graduate school are methodological in nature. More specifically, there isn’t a particular technique that is most useful, but rather a mode of thinking that allows me to approach problems in a rigorous fashion. Now, rigorous does not have to equate to statistical. Rather, it encompasses all the various methods by which we try to separate wheat from chafe, fact from fiction.