I am looking for papers / sources that outline any kind of general rule for a minimum sample size (real observations) for using the bootstrap.
For now I am using it to generate the CI for a mean. Would be nice to know if there are minimum 'n' suggestions for other operations like correlation etc. PM for code etc.
Dear Cory,
I hope the discussion with the link below may help you:
https://www.researchgate.net/post/Minimum_value_of_sample_size_to_bootstrap2
Best of luck,
Firdos
Dear Cory,
I'm not aware of any book or paper addressing this, but am quite confident there is no magic number as it depends on the data and research goal.
The assumption of bootstrap is that the sample is representative of the population, and the probability of this assumption holding is proportional to sample size. What happens when getting bootstrapped confidence intervals with small sample sizes?
RESOLUTION
Extremely small sample sizes, means bootstrap samples will just be repetitions of the same combinations. For example, bootstrapping 3 values give only 3^3 (27) possible combinations. If we try to get confidence intervals from those, there are only 27 possible values to choose from. Semi-parametric or parametric bootstrapping will smooth this out but make more assumptions...
SPURIOUS RESULTS
Doing bootstrapped confidence intervals with these will often result in spurious results (confidence intervals that exclude the population mean). A quick simulation matching the design of your project should be able to help guide you. Here's a quick demonstration using R for estimating a means and 95% C.I. from a population with a normal distribution N(0,1).
##### R code start #####
# define number of simulations
iter
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