I also have had pedagogical concerns. When we use the bootstrap for inference, we are essentially using it as some sort of approximation to the sampling distribution. Sampling distributions are hard enough without this added layer of complexity. I suspect that the bootstrap seems easier to students because they are just thinking, “95% of the bootstrap means are in the middle 95% of the bootstrap distribution,” which is just a tautology not relevant to understanding inference. So the bootstrap REPLACES understanding of sampling distributions.

While bootstrap t may work better it is not clear how this could be used to INTRODUCE inference as it seems to be dependent on learning t methods first.

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A sampling distribution shows us the dist of our sample stat. Then we look at and see where our particular sample is relative to all the possible samples and infer.

A bootstrap distribution gives us the same info but is made only from our sample. It’s kind of cool. I am in my 2nd semester with lock5.

I think you’ll like it.

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