You ever stare at a dataset so clean it almost makes you suspicious?

I was working on this internal report, years ago, a clean pull, plenty of rows, the kind of structure that makes your inner developer exhale. The task was simple: find out which section of a local news site was most engaging. Click-through rate, time on page, bounce rate, the usual suspects.

And the numbers spoke. Loudly. “Pets” crushed “Politics.” “Recipes” danced circles around “Real Estate.” The graph basically threw glitter at “Pets.” So I built the dashboard, shared it, and our PM lit up. “Let’s double down on Pet content,” he said. “People love it!”

But something itched. Not in the code, that part was fine. The itch was emotional, like a tune played in the wrong key. I asked him, “Wait, what do we mean by engagement? Is it a sign of interest, or a sign of escape?”

We pulled a random sample of pet article titles. “12 Puppies That Will Melt Your Brain.” “This Cat Knows Your Secrets.” It wasn’t engagement. It was dopamine.

The data hadn’t lied. But it had answered a shallow question, one we hadn’t realized was shallow until the numbers rang too loud.

It’s easy to believe in numbers when they confirm our instincts. That’s the seduction of quant work. It looks like truth because it’s tidy. But the question, the framing, that’s always messy. Human. Flawed.

I’ve seen this happen in code reviews too. Someone benchmarks a refactor, shows a 20% speed gain, and everyone nods. But no one asks what the user actually does on that feature. Or worse, whether it’s even used anymore. Speed gains on a ghost town.

Same with parenting. My daughter once asked how many minutes she could have on her tablet. I said thirty. She used a timer app to prove she had two minutes left, perfectly accurate. But I looked at her eyes, glassy and wired, and said “That’s enough.” Truth and usefulness aren’t the same thing.

Funny thing is, I still trust data. I build systems around it. But I’ve learned to hold the numbers gently. To ask what question they were answering, and whether that question still matters.

Most honest lies don’t come from malice. They come from clean math wrapped around crooked meaning.

And once you’ve seen that, you can’t unsee it.