4 Module 4
Confidence intervals via bootstrapping
Learning objectives
Define what a confidence interval is and why we want to generate one.
Explain how the bootstrap sampling distribution can be used to create confidence intervals.
Write a computer script to calculate confidence intervals for a population parameter using bootstrapping.
Effectively visualize point estimates and confidence intervals.
Interpret and explain results from confidence intervals.
Discuss the potential limitations of these methods.
4.1 Proposed structure of this chapter
This is work in progress! Please refer to readings listed on Canvas.
4.1.1 Introduction of confidence intervals
- Point estimate vs confidence intervals
- Box: percentiles and quantiles
- How to calculate it with bootstrapping
- Infer package code to get CI and plot them
4.1.2 Interpretation of confidence interval
- Explain technical definition of CIs: “X% of samples of size n will produce a X% CI that contains the true population parameter value”
- Explain simpler definition, and why we can’t say “CI represents the probability that the true parameter value is contained within the interval” (revisit random vs fixed values).
4.1.3 Properties of CIs
- Increase X% on width of CIs
- Increase sample size of width of CIs
4.1.4 Box: real example
a concrete example, with interpretation of CI.