I once published a host's "21% failure rate" after seven of a speed test's 33 nodes failed. Then the other three hosts returned the same 26/33 result: I had attributed the measuring service's failing probes to the host.
The arithmetic wasn't the problem. The explanation attached to it was.
That is the kind of mistake I'm interested in here: a number you could reproduce, but whose apparent meaning changed when you checked another target, inspected the response, or separated the measurement into phases.
Have you published or shared a number that turned out to describe your tools rather than the thing you were testing? A dashboard, internal report, code review, or benchmark result counts; it doesn't have to have reached a public article.
I'd like to hear:
- What did you initially think the number meant?
- What check made you change that interpretation?
- Did you correct the number itself, its label, or the conclusion people drew from it?
I'm especially curious about the last part. "These probes failed" can remain a valid observation even when "this host is unreliable" isn't supported by it. Keeping the observation while withdrawing the explanation seems useful, but it can be awkward once people have already acted on the original claim.
What did your correction look like, and what would have helped you catch the attribution error earlier?
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