The 5 Questions That Expose Bad Data Analysis (AI or Human)

Because the most dangerous analysis isn’t the one that looks wrong - it’s the one that looks right but isn’t.

You know that feeling when you're looking at analysis that seems professional, but something doesn't sit right? The charts look clean, the insights sound reasonable, but you can't shake the sense that something's missing.

That intuition is usually correct.

• Polished presentation often masks shaky foundations.

• Beautiful visualizations can hide flawed logic.

• Confident conclusions can rest on questionable assumptions.

The challenge isn't identifying obviously bad analysis - that's easy.

The challenge is spotting the subtle problems in work that looks legitimate but has fundamental issues underneath.

The Pattern Everyone Misses

Here's what happens in most organizations:

Analysis gets created quickly, reviewed briefly, and accepted if it looks professional.

The pressure to move fast means we focus on whether conclusions seem reasonable rather than whether the reasoning behind them is sound.

This works fine until it doesn't.

Until someone makes decisions based on analysis that looked good but was built on shaky ground. Until patterns emerge that don't match what the data actually showed.

The solution isn't slower analysis or more bureaucracy. It's knowing which questions reveal whether reasoning is actually solid, regardless of how polished the presentation looks.

Five Questions That Cut Through Polish

These aren't academic exercises. They're practical checks that work whether you're reviewing your own work, evaluating AI output, or sitting in a presentation. Each one catches different types of problems.