[dataflow]

Your comment is incredibly confusing (possibly misleading) because of the key details you've omitted.

> The book Superforecasting documented that for their best forecasters, rounding off that last percent would reliably reduce Brier scores.

Rounding off that last percent... to what, exactly? Are you excluding the exceptions I mentioned (i.e. when you're already close to 0% or 100%?)

Nobody is arguing that 3% -> 4% is insignificant. The argument is over whether 16% -> 15% is significant.

[btilly]

To the nearest 5%, for percentages in that middle range. It is not just 16% -> 15%. But also 46% -> 45%.

[dataflow]

Yes so this confirms my point rather than refuting it...

[btilly]

It seems that you reversed your point then. You said before:

Even before I read your comment I thought that 5% precision is useful but 1% precision is a silly turn-off, unless that 1% is near the 0% or 100% boundary.

However what I am saying is that there is real data, involving real predictions, by real people, that demonstrates that there is a measurable statistical loss of accuracy in their predictions if you round off those percentages.

This doesn't mean that any individual prediction is accurate to that percent. But it happens often enough that the last percent really does contain real value.