In practice these numbers wouldn’t perfectly match even if there was no correlation because there is some missing survey data that the SDO method ignores (because naturally you can’t sample data that doesn’t exist). In principle I don’t see why we shouldn’t use this as a good rule-of-thumb check for unacceptable correlation.
The synth distribution gives a geomean of 1.6%, a simple mean of around 9.6%, as per the essay
The distribution of all survey responses multiplied together (as per Alice p1 x Alice p2 x Alice p3) gives a geomean of approx 2.3% and a simple mean of approx 17.3%.
I’d suggest that this implies the SDO method’s weakness to correlated results is potentially depressing the actual result by about 50%, give or take. I don’t think that’s either obviously small enough not to matter or obviously large enough to invalidate the whole approach, although my instinct is that when talking about order-of-magnitude uncertainty, 50% point error would not be a showstopper.
In practice these numbers wouldn’t perfectly match even if there was no correlation because there is some missing survey data that the SDO method ignores (because naturally you can’t sample data that doesn’t exist). In principle I don’t see why we shouldn’t use this as a good rule-of-thumb check for unacceptable correlation.
The synth distribution gives a geomean of 1.6%, a simple mean of around 9.6%, as per the essay
The distribution of all survey responses multiplied together (as per Alice p1 x Alice p2 x Alice p3) gives a geomean of approx 2.3% and a simple mean of approx 17.3%.
I’d suggest that this implies the SDO method’s weakness to correlated results is potentially depressing the actual result by about 50%, give or take. I don’t think that’s either obviously small enough not to matter or obviously large enough to invalidate the whole approach, although my instinct is that when talking about order-of-magnitude uncertainty, 50% point error would not be a showstopper.