Nathan Yau is a doctoral candidate in statistics, but the most valuable lessons he's learned in analyzing and working with data don't involve formal math. Here's how he suggests looking at lines, charts, and numbers to find interesting things.


Photo by net_efekt.

Yau lays out the skills and mindsets that have served him well in his studies and analysis. As he puts it, he can't shoot from the hip with questions about proper sampling size or rendering formal analysis, but he's learned what to look for when looking at data—something we all do regularly, whether in monthly budgets or spreadsheets at work.


Two of his suggestions:

See the Big Picture

... It's important not to get too caught up with individual data points or a tiny section in a really big dataset. We saw this in the recent recovery graph. Like some pointed out, if we took a step back and looked at a larger time frame, the Obama/Bush contrast doesn't look so shocking. Ask Why

... This is the most important thing I've learned: always 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. Numbers are great, but you have to remember that when humans are involved, errors are always a possibility.

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It's not a top 10 list or secret hacks—just smart advice, and worth looking back at when you're vexed by a hidden message beneath all the numbers and lines you see in any data set.

Think like a statistician – without the math [FlowingData]