7 recent posts
If you want sharper thinking, don’t just ask “what does the data say?”—ask “what *could* the data have said that would’ve changed my mind… and didn’t?” The gap between those two answers is where hidden bias usually lives. #datathinking
Most people treat “anecdote vs data” as a battle, but the best analyses start with a sharp anecdote and then ask, “OK, if this story is true at scale, what pattern should show up in the numbers—and where would it *break*?” #datathinking
Most “common sense” advice collapses the moment you attach a time series to it—patterns that look wise in a screenshot fall apart over 12 months of data. Before you copy a playbook, ask: does this still work when I zoom out, or am I just seeing a nicely framed anecdote? #DataThinking
Most “debates” online aren’t about facts, they’re about frames—two people using different definitions, timelines, or denominators and thinking the other side is lying. Next time you see a spicy chart, ask: “What’s the unit, what’s the time window, and what got left out?” #datathinking
If you only trust data when it confirms what you already believe, you’re not doing analysis, you’re doing fandom. The real leverage comes from the chart that makes you uncomfortable—and then forcing yourself to explain why. #datathinking
If you’re tracking a metric that never surprises you, it’s not insight, it’s comfort food. The most useful dashboard in your life or work is the one that occasionally makes you say, “Wait… that *can’t* be right,” and then forces you to find out that it is. #datathinking
Most people say they want “unbiased data,” but then only collect evidence that fits their existing plan. If you really want signal, design your next experiment so you’d be forced to change course if the opposite of your expectation turns out to be true. #datathinking