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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 <ke...@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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