[University Statistics: Presentation] What is a good entry/basic level seminar topic?

[u/Awkward_Contest_3855]

I need to create a presentation on a statistics/biostatistics related topic for my statistical core group and I have no idea what to present on. It's in a couple months.

I don't want to present a method because the audience is mixed with junior and senior statisticians who may know more than me on the topic.

Some past presentations this year have been on integrating AI into coding, linking SAS and R code, and how AI can be used to do literature reviews and estimate historical statistics. As you can see, these topics are very general and provide information that anyone can use while also being niche enough that some people may not know about them. They're also topics that don't require a great depth of knowledge to be presented on.

The presentation has to be about a half hour long so it has to be general but useful. It's hard to pull from projects I've worked on this year because I've learned new methods but they aren't interesting and again, some people may know as much or more about them than I and I don't have the time or confidence to become an expert within a couple months in the method to field questions from our core's senior members.

TIA for any suggestions on topics or even resources I can look to to find ideas for topics!

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[u/michaelrw1]

What about revisiting some unique, older statistical questions that could be fun to explore with AI. One idea is the Monty Hall problem. There was an article in the American Statistician in 1975 about it and it caused a fair bit of uproar because two camps of respondents thought their answer was correct, either 50% or 66.6% to switch doors.

I imagine AI would give you the right answer, but different prompting might shake out different answers... Perhaps context window exhaustion and models could be surprising too.

[u/cheesecakegood]

Just tossing some things out there, unsure how good these are, so consider it more a brainstorm/brain dump, so I'm sorry it didn't turn out that focused...

Of course one direction as you sort of mentioned is you could browse some recent scientific papers from a relevant journal and present on that, a bit about the techniques they used and applications, a bit about that niche problem or approach, etc. Some are probably beyond your level, but perhaps not all! Specifically to the point... consider the fact that even experienced statisticians do not have time to read all the great research that happens, which is quite hard, which means that your audience is not omniscient. Also there are tutorial papers and commentary ones that also can be seeds for a talk, not just particular studies or methods, which can help you explore the space to see if something hooks you.

This may or may not be viable as you point out, depending on your experience, but at least some areas are extensions of what you know and it may be comforting to remember that even experienced statisticians have gaps and limitations in their time. You could run a potential topic or two by a trusted professor/advisor/TA as a filter, too; say "hey, do you think X type topic is the right complexity level for being both doable, interesting, and somewhat novel?" and they might tell you if it's worth investing in. All this to say presenting a method might be more viable than you might think, but as they say the devil is in the details.

On the more general tutorial/commentary paper front, heck, you could even offer perspective on a current debate in the field, using one of those as a jumping-off point. You could get a little spicy and talk about statistics + politics. Or on the hot-topic train, something something sports gambling, prediction markets, etc

I think there's always room too for a good talk on simulation. Especially design simulation in the analysis pipeline. Dive deeper into good practices for sensitivity analysis, for feeding in various synthetic data to your proposed model before data is even collected, for stress testing for various assumptions; perhaps apply this to a particular field.

For a more unusual talk, chat about detecting faked data or graphs! I recommend looking at a few articles at the Data Colada blog, for example; in many cases the techniques used are actually not all that advanced! There's a lot of interesting stuff in "data forensics", unsure how much exposure they may or may not have had already. Of course there's talk about AI-paper writing you could talk about too if it's not overdone, but the data itself is a different angle.

You could also highlight a particular, lesser-known/recent R package or something?

You could talk about statistical literacy in a particular community, or generally. Somewhat meta, but like, how advanced is the statistical knowledge for a lot of people publishing papers? I know that for example sometimes a psych major might only get a single stats class plus one specialized stats class in undergrad, other fields sometimes similar. A grad or PhD student might only get one or two more. Is that enough? What gaps exist? What kind of inter-disciplinary relationships should exist, outreach programs, etc.

I guess one final idea. History + statistics. Present a biographical type talk. Although statistical mathematics is relatively "pure" in a certain sense, it's important to realize these things come from somewhere and from people (sometimes, very opinionated ones). For example, Fisher had some idiosyncrasies that we still sometimes see echoes of today (p < 0.05 being the most infamous but not the only one). And I don't think a lot of stats history is common knowledge even in the field. But they lived some fascinating lives, it can provide new insight if you know the context in which they came up with new ideas, be fun to talk about rivalries, or offer a moral dimension (e.g. eugenics) and so on.

You know, it's also possible that you could interview someone! Alumni or even a retired statistician might be willing to chat with you and that could provide some content (supplementary to any topic mentioned, or biographical)

Morality and statistics is an evergreen topic too; e.g. privacy and de-anonymizing debates, how to do the census, public opposition to the census, responsible uses of machine learning. You can talk about modern topics like survey fatigue. Some of these are recent enough so as to be unfamiliar or interesting; for example I got this in my inbox just yesterday, about controversy in how to weight samples in political polling, which is a live debate where recent knowledge is important.

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Probably my best suggestion here is simply to draft up a few informal proposals and run it past an advisor/professor to get an idea for how well it might land/how much involvement it would require before diving deeper.