It sounds like you’re doing subsampling. Bootstrapping is random sampling with replacement.
If, for example, we kept increasing the size of the sample we draw, then eventually the variance would be guaranteed to go to zero (when the sample size equals the total number of forecasters and there is only one possible sample we can draw).
With bootstrapping, there are NN possible draws when the bootstrap sample size is equal to the actual sample size N. (And you could choose a bootstrap sample size K>N.)
Ah snap! I forgot to remove that paragraph… I did subsampling initially, then switched to bootstrapipng. Resulsts remained virtually unchanged. Thanks for pointing that out, will update the text.
It sounds like you’re doing subsampling. Bootstrapping is random sampling with replacement.
With bootstrapping, there are NN possible draws when the bootstrap sample size is equal to the actual sample size N. (And you could choose a bootstrap sample size K>N.)
Ah snap! I forgot to remove that paragraph… I did subsampling initially, then switched to bootstrapipng. Resulsts remained virtually unchanged. Thanks for pointing that out, will update the text.