How do bootstrap confidence intervals really tell us about the population?

[Aaron_J]

I'm analyzing a small dataset from a pilot study and my supervisor suggested I use bootstrap confidence intervals instead of relying on traditional parametric assumptions. I ran the resampling in R, but I'm a little uneasy—how can randomly resampling my own data a thousand times actually tell me something new about the population? It feels a bit like cheating.

[Harper.W]

Bootstrapping is not magic. It uses your data as a stand in for the population. Resampling with replacement builds a distribution of the statistic you care about so you can get a confidence interval without assuming a normal shape. It might feel like cheating but it is a way to simulate what would happen if you could sample again from the population. What exact estimate are you computing mean median or a regression coefficient

[DennisM]

Think of your sample as a tiny universe that contains the variation you might see in the real population. Resampling from it lets you see how sensitive your estimate is to the data you collected. If the data are IID and the sample not biased the bootstrap distribution tracks the sampling distribution reasonably well. Do you know whether your data meet the IID assumption

[Paisley65]

Be careful with dependence. If you have repeated measures time series or hierarchical data plain bootstrap can mislead you. A block bootstrap or cluster bootstrap is the way to go. Are your observations independent or is there a grouping structure

[PaulQW]

Different bootstrap flavors matter. The percentile bootstrap is simple but can be biased with skewed data. BCa bias corrected and accelerated tends to fix that but needs more setup. If you are using R check the boot package or the infer package just be mindful of assumptions. Do you plan to compare a couple of methods to see how they land

[MichaelT]

Use diagnostics. Compare bootstrap CIs to analytic ones when possible. If they diverge you know your data are skewed or small. A quick simulation study on your data context can help you gauge whether the interval makes sense. Want a quick checklist to verify your setup

[Brandon96]

In short bootstrap is a tool not a guarantee. It can reveal uncertainty you would not see otherwise but it does not fix bias. The key is understanding when it works well and when it does not. What kind of estimate are you focusing on and what surprised you about your bootstrap results