This page contains links to journal articles, blog posts, webpages, etc., that we (the instructors) believe may be useful to you, or at the very least interesting. Bold articles are ones that we consider must-reads but don’t have enough time to actually assign. And to be clear, must-read means that people will probably assume you’ve read this and know the basic points.
Lord (1953) “On the statistical treatment of football numbers.” – This one is worth reading several times, ideally with a friend. Don’t worry, it’s quite short. A small note: “Tchebycheff” is sometimes written as “Chebyshev” and in either case is pronounced Cheb-ee-sheve.
Malone & Goldstone (2019) “Episode 936: The Modal American.” – This episode of NPR’s podcast, Planet Money, is a great discussion of what we mean when we say “average,” why the mean can be a bad measure of central tendency, and how complicated calculating simple statistics are. They don’t use the words “artifact” or “collinearity”, but they discuss both and how they can lead to bad answers.
Rohrer (2019) “Indirect Effect Ex Machina.” – come for the ice cream and sauerkraut, stay for the creeping dread that many mediation studies are probably wrong.
Chester & Lasko (2019) “Construct Validation of Experimental Manipulations in Social Psychology: Current Practices and Recommendations for the Future.” – a totally brand new preprint about construct validity in social psychology. They do a great job of explaining different kinds of validity in addition to empirically describing the extent to which social psychologists check for construct validity. Here is a lovely quote: “In the context of archery, construct validity is the practice of checking that the arrow hit the bullseye and internal validity is the practice of preventing the wind and your own breathing from influencing your aim.”
Kahn Academy “Linear algebra.” For those looking for tutorials to practice matrix algebra. I’ve linked to the broader page because it includes linear combinations.
3Blue1Brown’s YouTube channel. Thanks to Scott Wallner for recommending these! There are playlists for linear algebra, calculus, and fractals, among others.
Fry (2019) “What statistics can and can’t tell us about ourselves.” This article in the New Yorker explains issues of (frequentist) probability in terms you can explain to your grandma, as well as makes clear the issues of the Fisher tradition of reducing experiments to p-values.
Resnick (2017) “What a nerdy debate about p-values shows about science – and how to fix it.” Another great article, this time on Vox, about p-values
Ioannidis (2005) “Why most published findings are false.” – This is one of the articles that kick-started the concern.
Bem (2011) “Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect.” – It’s true, ESP is real. Or is it? What (if anything) in this article makes you skeptical of the results? Try to articulate the weaknesses.
Bem (2004) “Writing the empirical journal article.” – This was assigned to me when I was a graduate student. Some of the advice is actually very good, although most of the best stuff can be found in Strunk and White. The part of this article that’s really worth reading is the “Analyzing Data” and “Reporting the Findings” subsections of “Planning your article.”
Szollosi et al. (2019). “Preregistration is redundant, at best.” Preprint.
Peters (2004) “The Zen of Python.” – A list of 19 principles for writing better Python code; the principles also apply to writing better R code.
Hill (2019) “Meet xaringan.” – There are a ton of resource for learning how to make slides using R. Too many to list here. I like this one because it’s accessible, funny, visually appealing, and will help you make slides that you’re proud of. (By the way, UO has theme you can use.)
Bryan (2019?) “Happy Git with R.” – Interested in using GitHub for version control? Jenny Bryan will guide you through the process and metaphorically hand you a tissue when you’re screaming with frustration.