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. > > > -- View this message in context: http://r.789695.n4.nabble.com/When-to-use-bootstrap-confidence-intervals-tp2326695p2326865.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
