[kenthorvath]

From the project README:

How bootstrapped works tldr - Percentile based confidence intervals based on bootstrap re-sampling with replacement.

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MIT OCW 18.05 has this to say about the technique:

https://ocw.mit.edu/courses/mathematics/18-05-introduction-t...

The bootstrap percentile method is appealing due to its simplicity. However it depends on the bootstrap distribution of mean(x') based on a particular sample being a good approximation to the true distribution of mean(x). Rice says of the percentile method, “Although this direct equation of quantiles of the bootstrap sampling distribution with confidence limits may seem initially appealing, it’s rationale is somewhat obscure.”

In short, don’t use it.

Use the empirical bootstrap instead (we have explained both in the hopes that you won’t confuse the empirical bootstrap for the percentile bootstrap).

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Updated to reflect suggestions in comments below.

[johnmyleswhite]

I'm not a big fan of the percentile bootstrap, but that reference is a little too cavalier with material that deserves to be treated more rigorously. Chapter 8 of Wasserman's "All of Statistics" is much more careful about outlining the conditions under which the percentile bootstrap will work. Moreover, he works through a specific example that demonstrates that the percentile bootstrap does not generate results that are profoundly different from other methods.

[spencebeecher]

John, you are a true wizard. I admire you & will work to incorporate your feedback (gathered offline) into the library =)

Thanks for the feedback!

[darkxanthos]

Just to make sure your comment is clear, the powerful portion of your quote "In short, don't use it." is strictly intended for the percentile bootstrap. Immediately after the paper says "Use the empirical bootstrap instead..."

[spencebeecher]

Yep! You are right. I don't think it is a huge disparity but I would like to implement the pivotal/empirical bootstrap instead. The change is just a few lines of code.