This is the start of a new feature round here! Well, new but yet…old.* 🙂 Meghan, Brian, and I have been blogging for 7 years and between the three of us we’ve written over 2000 posts. Many of those posts are still as relevant today as when we wrote them, but you’re not likely to stumble across them via casual browsing, social media, or the first page of a Google search. So on Saturdays, we’re going to start re-upping old posts. Hope you enjoy them and find them useful.
Today: what’s the origin of the term “field work”? Click through or keep reading for the answer! And then in the comments, tell us: in future should we just link to the old post, or repost it? Because it seems a little silly to do both…
Also this week: a fake Science paper replicates (?!), and more. Most of it not good. Sorry, it’s been that kind of week.
Recently we invited y’all to complete a reader survey, to see how we’re doing and help us better understand the reasons for the year-on-year decline in our traffic that began 11 months ago. Read on if you’re interested in the results, and my tentative thoughts on what to do in response to them.
As I’ve written about before, I am chairing a task force for Michigan’s Rackham Graduate School that is focused on graduate student mental health. We started our work last summer, and have spent the past several months especially focused on identifying needs. This was done by evaluating what others have found; in one-on-one conversations with graduate students, mentors, and mental health professionals; and by hosting a couple of town halls. This post summarizes the major themes that emerged out of these conversations.
One of the challenges with teaching introductory biostatistics is that there are so many online resources available to students. This creates a filtering problem: many of the resources that come up on the first page of a Google or YouTube search won’t necessarily be the best resources. Admittedly-anecdotal example: years ago, back when I still taught the Mann-Whitney U test (I don’t any more), I was alarmed to discover that the Wikipedia page for the Mann-Whitney U test contained serious mistakes.*
I want to assist my students with this filtering problem. Steer them towards resources I’ve looked at and am happy to vouch for. Which of course puts the filtering problem on my shoulders instead. And I don’t want to spend many hours reading stats websites and watching YouTube stats videos.
So that’s where you come in. In the comments, please suggest videos and other online resources (especially interactive resources) that you’ve found useful for teaching introductory statistics, or for learning introductory statistics yourself. I hope and expect that your collective experience and opinions will be a much better guide to the best stuff out there than Google’s PageRank algorithm is. If we get enough responses, I’ll organize them into a future post, like Meghan’s compilation of videos for teaching ecology.**
To kick things off, Michael Whitlock and Dolph Schluter have very nice interactive online tutorials for teaching sampling from a normal distribution, the Central Limit Theorem, and the interpretation of a frequentist 95% confidence interval for the sample mean. I use all these in my class and recommend them highly. They have a few other interactive apps I haven’t used but that I assume are also good.
Related old post: my compilation of “statistical vignettes“: easy-to-explain, dramatic illustrations of statistical concepts and their application to everyday life.
*Which I corrected; that’s only time I’ve tried to fix a Wikipedia page. I have no idea what the Wikipedia page for the Mann-Whitney U test is like today.
**Note that I’m looking here for resources for teaching statistical concepts, not R. “Online resources to teach R” would be a whole ‘nother post!
Also this week: the bibliometrics of ecology and evolution, Johns Hopkins vs. legacy admissions, and more.
Recently, a friend who was working on a grant proposal asked if I have the specific experiments in mind first and then come up with the framing from there, or if I have the big picture framing in mind and develop the specific experiments from there. I was a little stumped at first, then realized that was because I don’t really use either of those approaches. Instead, my initial motivation is usually preliminary data that I’m excited about and where it’s clear more work needs to be done to figure out what is really going on.
Here’s an example: As a graduate student, I carried out a study on a population where I tracked a parasite outbreak and host population dynamics and, at the same time, assayed the susceptibility of the population to that parasite at three time points. The results of the susceptibility assays were not at all what I expected at the start of the experiment:
The ASN standalone meeting features an evening debate between two pairs of people, taking opposite sides of some proposition. This year’s proposition was (paraphrasing) “It’s no longer possible to be a naturalist in a world on which humans are having such large effects.” As another example, the first debate several years ago considered (paraphrasing) “Species richness on continents reflects ecological not evolutionary limits.” At it’s best, with the right people (who take it seriously but not too seriously), it’s a great format. It’s a low-stakes way for people to air opposing viewpoints, in a way that both entertains outsiders and gets them thinking and talking.
The ASN is currently looking for topic suggestions for the next debate. So, got any ideas?
Here are a couple of opening bids:
- “Species interactions are not stronger and more specialized in the tropics”.
- “Ecologists and evolutionary biologists should stop pursuing fundamental research in order to focus on pressing applied problems”
Please do chime in with your ideas!
In an old post, we talked about scientific “one hit wonders”–scientists who made a single major contribution, but whose other work was not especially notable. In that post, I made the joking analogy to pop band Soft Cell and their hit “Tainted Love”. With which Jeff Ollerton quibbled, noting that while “Tainted Love” was Soft Cell’s biggest worldwide hit, Soft Cell actually had several other hits in the UK. Meaning that Soft Cell weren’t actually one hit wonders and really shouldn’t be remembered as such.
Soft Cell is far from the only such example, of course. The passage of time has a way of simplifying and flattening the memory of anybody. Wait long enough, and almost anybody who’s remembered at all will be remembered as a one-hit wonder.
Which got me thinking that it would be fun to talk about ecologists and other scientists who are remembered primarily for one thing, but who actually did other notable work.
Some opening bids:
I’m putting up this brief post to announce that the ESA Bulletin has published my paper, “A data-based guide to the North American ecology faculty job market“. This paper pulls together much of what I’ve written about this topic over the past few years in one place. I’m hopeful that this will make these data more useful to more ecology faculty job seekers, now and in future. I’ve received a lot of positive feedback on this work over the years from ecology faculty job seekers, expressing appreciation for data that addressed their anxieties. Receiving that feedback motivated me to keep pursuing this work and publish it in the Bulletin.
I’m also aware of some ethical concerns about the data I compiled on gender balance in recent ecology faculty hiring, that were raised at the time the preprint went up. I responded to some of those concerns at the time they were raised. Responding to other concerns required more time. I sought advice from knowledgeable colleagues (who are not responsible for my choices), consulted my institution’s IRB, and redid the data compilation using modified methods previously used in other recent papers addressing gender balance in other areas of ecology. The Bulletin paper thus differs from the preprint in some ways, and addresses the concerns of which I’m aware to the best of my ability. I recognize that my responses will not satisfy everyone.
For me, publication of this Bulletin piece brings this body of work to a close. I have no plans to continue data collection, or to do further analyses of the data I’ve already collected. I don’t think there’s much more of interest to be learned from these data. And the ecology faculty job market only changes slowly, so these data will remain a reliable guide for several years at least. The Bulletin piece is now out there for anyone who wants to read it; it’s time for me to move on to other things.