I have to be somewhat vague regarding data and such for confidentiality purposes (I'm not allowed to share whole data). I have a dataset (X) that represents transects (representing line transects of animals in space).
Birds area len transect 1 0.239310 0.478621 1 2 0.238463 0.476927 1 1 0.244382 0.488765 2 4 0.236501 0.473002 2 0 0.245832 0.491665 3 1 0.241026 0.482052 4
When I calculate the mean density of the birds, e.g.:
meandensity <- sum(X$Birds)/sum(X$area)
in the dataset, I get a mean value of ~3.63.
The problem is I've been given a MATLAB module written by someone else (and I can't read MATLAB code) who claims to be using a block bootstrap method to estimate mean and confidence intervals. When I run that module, the mean density gets estimated at ~4.6.
Now, I spoke to this person and have tried to replicate his bootstrap method and am getting a value of ~3.5.
My questions: Can the bootstrapped estimate of the mean really differ this much from the mean of the whole dataset? My understanding was that the bootstrap estimate shouldn't be super far from the mean, and differences between bootstrap methods would impact the CIs more than the estimated mean.