The danger of being almost right

There’s a kind of result that doesn’t raise any alarms.

It looks right.
It passes.
It feels trustworthy.

Nothing about it stands out as wrong.

And that’s the problem.

I used to think the biggest risks in testing and measurement came from obvious errors—bad data, poor effort, clear mistakes.

But over time, the cases that stuck with me weren’t like that.

They were the ones that made it all the way through.

Everything checked out.

And still, something was off.

For a while, I didn’t have a good way to describe it.

When I was younger, I used to joke that the title of my autobiography would be The Stretchy Middle—that space where things aren’t clearly right or clearly wrong, but somehow still hold together.

At the time, I was just trying to get a laugh.

I didn’t realize how close it was to something real.

The more I paid attention, the more I realized:

the most difficult problems weren’t at the extremes.

They were in the middle.

Not completely wrong.
Not obviously flawed.

Just… almost right.

That’s where things start to go wrong.

This is an attempt to understand why.