I often read blogs from professors of statistics talking about how they prefer to teach models from a "distribution-centric" perspective rather than otherwise. However, I've never really been able to understand this point and it's differences from other methods of communicating models.

Could anyone provide an explanation for what is meant by this and provide a simple example (say a general linear model) and a more complex one (say a mixed-effects model)? Any literature which discusses the differences in pedagogy would also be immensely helpful.

I am a postgrad biologist who does some undergrad statistics tutoring, and I am very keen to learn new ways to approach teaching these topics.

  • $\begingroup$ Can you share the links to a couple of these blog posts? It's hard to answer your question in the absence of such links. $\endgroup$ – Isabella Ghement Mar 24 '18 at 19:16
  • $\begingroup$ ¿Can you also provide examples of what is meant by "otherwise"? ¿What are some alternatives that you are thinking? I might suggest a research-question focus for the discussions, but I'm not sure if this aligns with your interests. $\endgroup$ – Gregg H Apr 29 '18 at 14:09
  • $\begingroup$ Basically they're tangential comments, so I can't link anything definitive. But for example I have seen it mentioned by Ben Bolker and Brian McGill in blog posts discussing mixed and hierarchical modelling $\endgroup$ – NatWH Apr 29 '18 at 14:21

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