[R] OT: A philosophical question about statistics

[Sergei Ko]

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 using 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 using 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
> >
> >
> >
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> code.______________________________________________
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