But the method also has its critics. You use the same numbers over and over again until you get an answer that you can't get any other way. In order to do that, you have to assume something, and you may live to regret that hidden assumption later on. Efron and others who study and use the bootstrap method concede that it doesn't work for every statistical problem. Nonetheless, Efron says, it works remarkably well in many different situations, and further theoretical work will help clarify its limits.
Statistics education ought to concentrate on understanding data and variation.
There's a movement away from black box calculation and toward more emphasis on reasoning-more emphasis on actual experience with data and automating the calculations so that the students have more time to think about what they're doing. But bringing innovative ideas into the statistics classroom remains a slow process. Part of the problem, says Moore, is that many statistics courses are taught not by statisticians but by engineers, mathematicians and others who learned statistics the old way and haven't seen firsthand how the computer has revolutionized statistical analysis. Statisticians work with data all the time. Hence, experienced statisticians are probably more inclined to accept teaching methods that rely on large amount of computation rather than on manipulation of standard formulas.