At a guess, they think claiming that an individual increased revenue by 93% using a simple balancing method is not very plausible, and as it is most likely just and outright lie can thus be discarded as hot air (which is actually pretty charitable).
One objection is of course that 93% is very achievable if the numbers involved are very small, but in that case the statement might just be code for "selling the office sofa" which is often seen as less impressive.
Neither really makes sense from a statistical perspective: your number is effectively the true value, and there is no repeatable measurement process you’re evaluating.
Your use of the non-statistical meaning of “accurate” as “free from error” is spot on.
No, the number is given in a causal statement, it is not simply the reporting of a measurement as you claim. So the distinction between "accurate" and "precise" is definitely relevant.
No one really doubts that the revenue increased. The question is whether the claim that it was by aligning the blah-de-blahs with the foobars is an accurate one and, related, what the individual contribution/leadership was. (Could another peer make the exact same claim with similar credibility?)
In this situation I was actively evaluating support issues, customer reported problems and new feature requests to triage what was worked and when. I’m comfortable suggesting that if somebody else was making those decisions at the time, the entire company would have folded. It’s hard to expand further on that without going into a lot more detail though.
The fact that the impact is generic, and you can not be sure about that the candidate's actions were the true cause?
In this case would a more specific claim with a more solid reasoning help?
Or are you dismissing all claims of potential positive impacts?