6. Bootstrapping

The sample as a proxy for the unknown population. Sample from said proxy population (i.e., the sample) to generate a sampling distribution. Bootstrap.

Author

Johanna Hardin

Published

October 7, 2024

Artwork by @allison_horst.

Agenda

October 7, 2024

  1. Review: logic of confidence intervals
  2. Logic of bootstrapping (resample from the sample with replacement)
  3. BS SE of a statistic

October 9, 2024

  1. Normal CI using BS SE
  2. Bootstrap-t (studentized) CIs
  3. Percentile CIs
  4. properties / advantages / disadvantages

Readings

Reflection questions

  • Why would anyone ever want to bootstrap?

  • What are the differences between a normal CI with Boot SE, a Bootstrap-t CI, and a percentile interval?

  • Why do we need to bootstrap twice for the Bootstrap-t CI?

  • What makes a confidence interval procedure good?

Ethics considerations

  • Why isn’t the bootstrap method a solution for the situation of small sample sizes?

  • Why isn’t the bootstrap method a solution for the situation with biased / unrepresentative data?

  • Consider a population with a maximum value (the parameter of interest). Will the sample max have a sampling distribution which is centered on the true maximum? Why or why not? [Quintessential example of how a statistic can be biased for the parameter.]

Slides

Additional Resources

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