[R] OT: A philosophical question about statistics

[Gregg Powell]

Sent from Proton Mail Android

Kevin, 
Looks like I sent the wrong URL 
Try this instead. 

https://www.biopharmaservices.com/blog/statistical-methods-the-conventional-approach-vs-the-simulation-based-approach/#:~:text=Both%20simulation,reliability%20of%20their%20statistical%20analyses

Best regards, 
Gregg
-------- Original Message --------
On 5/6/25 06:14, Kevin Zembower <kevin using zembower.org> wrote:

>  Gregg, thanks for your reply to my questions. I was looking for exactly
>  the type of information you included, especially on the strengths and
>  weaknesses of each approach.
>  
>  I was very pleased with the intuitive aspects of the simulation
>  approach in my course. This was the part that was missing from my first
>  exposure to statistics many years ago. Then, I thought of statistical
>  formulas as 'black boxes,' where numbers were fed in and results came
>  out, with unknown processes operating in between. With simulations, I
>  could count dots on a chart and come up with meaningful results.
>  
>  I missed the connection to my questions at the website you referred me
>  to, biopharmaservices.com. This seems to be the home page of a firm
>  that conducts medical studies, but I couldn't find anything about the
>  practical use of statistics. Perhaps I didn't search enough.
>  
>  Thanks, again, for your thoughts and perspective.
>  
>  -Kevin
>  
>  On Mon, 2025-05-05 at 16:05 +0000, Gregg Powell 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
>  
>  
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