[R] When to use bootstrap confidence intervals?

[Shentu]

Just based on my limited understanding of bootstrapping and statistics in
general, bootstrapping is effective but not magical - you can't reasonably
expect any reliable inference to be drawn about the population based on a
sample of 10, without any distributional assumptions. Your t interval looks
good conditional on the fact that you know what distribution you used to
simulate the data.   

Mark Seeto wrote:
> 
> Hello, I have a question regarding bootstrap confidence intervals.
> Suppose we have a data set consisting of single measurements, and that
> the measurements are independent but the distribution is unknown. If
> we want a confidence interval for the population mean, when should a
> bootstrap confidence interval be preferred over the elementary t
> interval?
> 
> I was hoping the answer would be "always", but some simple simulations
> suggest that this is incorrect. I simulated some data and calculated
> 95% elementary t intervals and 95% bootstrap BCA intervals (with the
> boot package). I calculated the proportion of confidence intervals
> lying entirely above the true mean, the proportion entirely below the
> true mean, and the proportion containing the true mean. I used a
> normal distribution and a t distribution with 3 df.
> 
> 
> 
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