I've often wondered how the field of statistics, and statistical education, would have evolved if modern-day computers and software and programming were available in the early years. Would the "traditional" methods, requiring simplifying assumptions, have been developed at all?
--Chris Ryan avi.e.gr...@gmail.com wrote: > A brief answer to this OT question is that many disciplines do the same > thing and teach multiple methods, including some that are historical and are > no longer really used. > > But since you say this was an intro course, it would not prepare you well if > later courses and the real world expose you to uses of the other methods > such as being asked to maintain or extend applications already in use from a > while back that use one or another or combinations. > > As others have noted, this is not really a case of either/or. It is both. > Would you make US students choose between knowing the metric system and the > one more commonly used now? I see many things labeled with both kinds of > measures, including car speedometers. > > > -----Original Message----- > From: R-help <r-help-boun...@r-project.org> On Behalf Of Bert Gunter > Sent: Monday, May 5, 2025 3:09 PM > To: Ebert,Timothy Aaron <teb...@ufl.edu> > Cc: R-help email list <r-help@r-project.org>; Kevin Zembower > <ke...@zembower.org> > Subject: Re: [R] OT: A philosophical question about statistics > > Heh. I suspect you'll get some interesting responses, but I won't try to > answer your questions. Instead, I'll just say: > > (All just imo, so caveat emptor) > > 1. What you have been taught is mostly useless for addressing "real" > statistical issues; > > 2. Most of my 40 or so years of statistical practice involved trying to > define the questions of interest and determining whether there existed or > how to best obtain relevant data to answer those questions. Once/if that > was done, how to obtain answers from the data was usually straightforward. > > Cheers, > > Bert > "An educated person is one who can entertain new ideas, entertain others, > and entertain herself." > > > On Mon, May 5, 2025, 18:12 Ebert,Timothy Aaron <teb...@ufl.edu> wrote: > >> (adding slightly to Gregg's answer) >> Why do professionals use both? Computer intensive methods (bootstrap, >> randomization, jackknife) are data hungry. They do not work well if I have >> a sample size of 4. One could argue that the traditional methods also have >> trouble, but one could also think of the traditional approach as assuming >> unobserved values. Assuming that the true distribution is represented by > my >> 4 observations then ... >> Computer intensive approaches have not been readily available until the >> invention of widely available faster computers. There is a large body of >> information and long experience with the traditional methods in all >> scientific disciplines. If you are unfamiliar with these approaches, then >> you may not fully understand that key paper published 30 years ago. >> We like to think we have "the answer" but there are times where the >> answer we get depends on how we ask the question. The different tests ask >> the same question in different ways. Does the answer for your data change >> depending on what approach is used? If so, then what assumption or which >> test is problematic and why? >> >> Tim >> >> >> -----Original Message----- >> From: R-help <r-help-boun...@r-project.org> On Behalf Of Gregg Powell via >> R-help >> Sent: Monday, May 5, 2025 12:06 PM >> To: Kevin Zembower <ke...@zembower.org> >> Cc: R-help email list <r-help@r-project.org> >> Subject: [R] OT: A philosophical question about statistics >> >> [External Email] >> >> 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://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat >>> .ethz.ch%2Fmailman%2Flistinfo%2Fr-help&data=05%7C02%7Ctebert%40ufl.edu >>> %7C17e2085007584244e78708dd8beebce9%7C0d4da0f84a314d76ace60a62331e1b84 >>> %7C0%7C0%7C638820579678440788%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGki >>> OnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ >>> %3D%3D%7C0%7C%7C%7C&sdata=C26Jn2LVk5CW1IXEglWxFRCuLfjC7LB3p6QBH2KkVCI% >>> 3D&reserved=0 PLEASE do read the posting guide >>> https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww. >>> r-project.org%2Fposting-guide.html&data=05%7C02%7Ctebert%40ufl.edu%7C1 >>> 7e2085007584244e78708dd8beebce9%7C0d4da0f84a314d76ace60a62331e1b84%7C0 >>> %7C0%7C638820579678469839%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRy >>> dWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D% >>> 3D%7C0%7C%7C%7C&sdata=arwwwchCqqRHcCLVTXQSfneEUX2yp6ucFp%2B4IBhrkv8%3D >>> &reserved=0 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. > > ______________________________________________ > 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.
