David Diez, OpenIntro
The percentile bootstrap approach has made inroads to introductory statistics courses, sometimes with the incorrect declaration that it can be used without checking any conditions. Unfortunately, the percentile bootstrap performs worse than methods based on the t-distribution for small samples of numerical data. I would wager that the large majority of statisticians proselytize the opposite to be true, and I think this misplaced faith has created a small epidemic.
The percentile bootstrap is nothing new, but its weaknesses remain largely unknown in the community. I find myself wrestling with several considerations whenever I think about this topic.
Chris Malone – Winona State University
One’s success in a course is often determined by his or her desire and motivation to learn. Unfortunately, desire and motivation are often lacking in an introductory statistics course. I have learned some tricks over my years of teaching to enhance motivation — leverage their existing knowledge whenever possible and require students to repeatedly consider the phrase “What would happen if … .”
Modern technologies and the recent advances in the use of simulation-based methods in teaching introductory statistics have allowed students to easily consider a variety of “What would happen if …” scenarios.
Karsten Maurer – Iowa State University
In this post, I provide my opinion on whether or not we should teach the bootstrap in introductory statistics courses. I think this question is best answered in two parts: (1) can introductory students generally understand bootstrapping concepts and (2) is the additional bootstrapping material beneficial for student learning. The first component is effectively questioning “can we?” which is necessary before we try to answer the question, “should we?” My short answer to both of these is an emphatic, yes! We can and should teach the bootstrap in introductory statistics courses. My slightly longer answer follows in the remainder of this post.
My short answer … is an emphatic, yes!
Tim Hesterberg – Google
Here are some arguments for why we should not use bootstrap methods and permutation tests in teaching Stat 101:
- Our usual cookbooks of formulas is such a resounding success, inspiring generations of students to further study (and rewarding their instructors with stellar reviews),
Bootstrapping and permutation tests make hard abstract concepts like sampling distributions, p-values, standard errors, and confidence intervals more concrete;
Beth Chance, Nathan Tintle, and the ISI team
Although we strongly agree that we must do more to help students understand the role of sampling variability in inferential decisions, we have not yet been convinced that a formal treatment of bootstrapping (having students sample with replacement) is the only path to get them there.
we worry that the motivation for conducting bootstrapping is less intuitive for students