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 ______________________________________________ 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.
