Bootstrap strange estimate

Hello everyone, I'm a statistical novice, I would like your help. I'm analyzing non-normally distributed variables. To develop the confidence interval I preferred to use non-parametric method: bootstrap.
Unfortunately I get output biased estimates and standard errors high, even though I used a lot of replies. How can I do?


Less is more. Stay pure. Stay poor.
Tell us more about what you are doing. Confidence interval for what? Also, perhaps post a histogram of your BS estimate.
this is R code:



bfer=boot(ferr, mean.func, R=5000)


Less is more. Stay pure. Stay poor.
So you are just putting 95 percentile CI on your means? The histograms and q-q plots look fine. What is your concern?


Less is more. Stay pure. Stay poor.
Well you can see that it is symmetrical and that is the purpose of the bootstrap. The bootstrap cant just make your se smaller the more samples you use like increasing the sample size in traditional approaches. I actually remember hearing a Prof say that with more samples the SE will get wider with BS, not sure if that is case specific. A general rule of precision is your CI is < 30% of your estimate, but that is just an arbitrary rule.