>From a practitioner perspective. Parametric methods have more power. If assumptions are here - use formulas. On the other hand my usual recommendation to colleagues: "If you don't know what to do - use bootstrap."
Regards, Sergiy On Mon, 5 May 2025, 17:06 Gregg Powell via R-help, <r-help@r-project.org> wrote: > Hi Kevin, > It might seem like simulation methods (bootstrapping and randomization) > and traditional formulas (Normal or t-distributions) are just two ways to > do the same job. So why learn both? Each approach has its own strengths, > and statisticians use both in practice. > > Why do professionals use both? > Each method offers something the other can’t. In practice, both > simulation-based and theoretical techniques have unique strengths and > weaknesses, and the better choice depends on the problem and its > assumptions (check out - biopharmaservices.com). Simulation methods are > very flexible. They don’t need strict formulas and still work even if > classical conditions (like “data must be Normal”) aren’t true. Theoretical > methods are quicker and widely understood. When their assumptions hold, > they give fast, exact results (a simple formula can yield a confidence > interval, again, check out - biopharmaservices.com). > > Advantages of each approach > • Simulation-based methods: Intuitive and flexible. They require fewer > assumptions, so they work well even for odd datasets. > • Theoretical methods: Quick to calculate and convenient. Based on > well-known formulas and widely trusted (when standard assumptions hold). > > Why learn both? > Knowing both makes you versatile. Simulations give you a feel for what’s > happening behind the scenes, while theory provides quick shortcuts and > deeper insight. A statistician might use a t-test formula for a simple case > but switch to bootstrapping for a complex one. Each method can cross-check > the other. Mastering both approaches gives you confidence in your results. > > Will future students learn both? > Probably yes. Computers now make simulation methods easy to use, so > they’re more common in teaching. Meanwhile, classic Normal and t methods > aren’t going away – they’re fundamental and still useful. Future students > will continue to learn both, getting the best of both worlds. > > Good luck in your studies! > gregg > > > > On Monday, May 5th, 2025 at 8:17 AM, Kevin Zembower via R-help < > r-help@r-project.org> wrote: > > > > > > > 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.______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
