# Thread: Bootstrap strange estimate

1. ## 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?

2. ## Re: Bootstrap strange estimate

Tell us more about what you are doing. Confidence interval for what? Also, perhaps post a histogram of your BS estimate.

3. ## Re: Bootstrap strange estimate

this is R code:

attach(x)
x

library(boot)
mean.func=function(x,ind)
median(x[ind])

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

boot.ci(bfer)

4. ## Re: Bootstrap strange estimate

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

5. ## Re: Bootstrap strange estimate

my doubt is the st error....too high. can I present these values? I am not shure

6. ## Re: Bootstrap strange estimate

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.

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