I explore the mysteries of the mind/brain and how it records perceptual information from the world, especially information contained in language and in music. How do different perceptual (and cognitive) histories shift your current perceptual experiences? What kinds of learning facilitate other kinds of learning? Does learning have a qualitatively different impact when it is early in development, rather than later in life?
Graphs are a great way to summarize your data. In fact, I often start reading a paper by scanning through the graphs, as do several of my colleagues.
But not all graphs are created equal. I find myself making the same comments on graphs, figures, depictions of data over the years (and, okay, I made these mistakes myself for a long time) so I thought I would condense them here. If you aren’t my student, you can write this off as the rantings of a (literally) myopic academic, but I suspect you’re going to hear it from someone else. Unless you’re a grapherator for the New York Times, in which case you may be shielded from myopic academics somewhat.
(1) Tiny font. There, see how annoying that is? The default font sizes for most programs (Excel, R, others) are often too small, especially when you reduce the figure to fit into your paper. Same goes for blurry or pixelated font.
(2) Mysterious labels. Numeric labels (“1”, “2”) or shorthand labels that made sense to you and maybe your professor (“COND1”, “REV”) do not make for easy interpretation of figures. Choose clear names that communicate your meaning without anyone having to read through the text to figure it out.
(3) Different scales on otherwise-identical figures. Yes, I know it looks prettier if you rescale the y axes across figures so that the results appear more similar, or so that the data take up the maximum amount of space in the plot. The problem is that it sets up false similarities, or perhaps false differences, across figures. Your duty is NOT to make things pretty. The duty of every
Starfleet officer scientist is to the truth .
Good luck making some sweet graphs!
On flashy research
I’d love to have research that is flashy to more people than a grouchy set of reviewers at Journal X. That said, I do spend time wondering if this is true (the page’s author quotes an email from Dan Kahan):
“…another problem is the “wtf?!!!!!!” conception of psychology. Its distinguishing feature is its supposed discovery of phenomena that are shocking bizarre & lack any coherent theory.
The alternative conception of psychology is the “everything is obvious — once you know the answer.” The main point of empirical research isn’t to shock people. It’s to adjudicate disputes between competing plausible conjectures about what causes what we see. More accounts of what is going are plausible than are true (emphasis SCC); without valid inference from observation, we will never separate the former from the sea of the latter & will drown in a sea of “just so” story telling.
I often get a little grouchy when I talk about my research and people say, “Well…that seems obvious.” Of course it does, now that I told you about it.
Bonus–Kahan, the commenter who said this is a dead ringer for Dwight Schrute.
You *do not* have to work 80 hours.
I’ve thought this for a long time.
Writing good, constructive reviews is hard–almost as hard as reading them. Here are some tips for writing reviews well.
- One piece of advice that I really liked was to write the review as though you’re writing it for a colleague or your own student. This forces you (one hopes) to be honest yet constructive.
- Ten tips on review writing from Brian Lucey
- And if ten isn’t enough, twelve tips from Henry Roediger III
Maybe later I’ll post some egregious reviewer comments that I’ve collected over the years. Three common themes:
- Insulting the author’s level of experience by implying that some senior person should rewrite the paper, or saying that a paper is a “nice student paper.” It’s either a nice paper or it’s not a nice paper.
- Not liking the theoretical implications of the results, so stating that said results are either so obvious as to be uninteresting, or the outcome of an extremely flawed paradigm (sometimes both).
- Suggesting a different analytic technique. While this is sometimes licensed, it often (to me) feels like blind adherence to statistics fads–analyses that people try out that may offer improvements over other more standard techniques, yet introduce coding and interpretation problems of their own (including how to get the reader to interpret the results accurately).