Resampling methods in statistics have been around<br>for a long time. Over forty years ago Tukey coined<br>the term jackknife to describe a technique, at-<br>tributed to Quenouille (1949), that could be used to<br>estimate bias and to obtain approximate con dence<br>intervals. About 20 years later, Efron (1979) intro-<br>duced the bootstrap" as a general method for esti-<br>mating the sampling distribution of a statistic based<br>on the observed data. Today the jackknife and the<br>bootstrap, and other resampling methods, are com-<br>mon tools for the professional statistician. In spite of<br>their usefulness, these methods have not gained ac-<br>ceptance in standard statistics courses except at the<br>graduate level. Resampling methods can be made<br>accessible to students at virtually every level. This<br>paper will look at introducing resampling methods<br>into statistics courses for health care professionals.<br>We will present examples of course work that could<br>be included in such courses. These examples will<br>include motivation for resampling methods. Health<br>care data will be used to illustrate the methods. We<br>will discuss software options for those wishing to in-<br>clude resampling methods in statistics courses.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education