I marked this posting as Off Topic because it doesn’t specifically apply to R and Statistics, but is rather a general question about statistics and the teaching of statistics. If this is annoying to you, I apologize.
As I wrap up my work in my beginning statistics course, I’d like to ask a philosophical question regarding statistics. In my course, we’ve learned two different ways to solve statistical problems: simulations, using bootstraps and randomized distributions, and theoretical methods, using Normal (z) and t-distributions. We’ve learned that both systems solve all the questions we’ve asked of them, and that both give comparable answers. Out of six chapters that we’ve studied in our textbook, the first four only used simulation methods. Only the last two used theoretical methods. My questions are: 1) Why don’t professional statisticians settle on one or the other, and just apply that system to their problems and work? What advantage does one system have over the other? 2) As beginning statistics students, why is it important for us to learn both systems? Do you think that beginning statistics students will still be learning both systems in the future? Thank you very much for your time and effort in answering my questions. I really appreciate the thoughts of the members of this group. -Kevin ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
