Which college majors have exploded in popularity over the past decade and which have shrunk? Over the past two posts (#1, #2) I’ve discussed my analyses of the number of degrees awarded in various fields at US “R1” universities (“very high research activity”) between 2011 and 2021. I’ve wondered how I could convey the overall … Continue reading College Major Trends: The Movie
In my last post, I plotted trends in various college majors, comparing how the numbers of degrees awarded in a range of subjects have grown or shrunk over the decade 2011-2021. I compared my university, the University of Oregon, and other R1 (“Very high research activity”) universities. This post focuses on computer science and closely … Continue reading 10 Double the number of Computer Science majors / 20 GOTO 10
About four years ago, I looked into data on how US college students’ choice of majors had changed since 2011 and whether the University of Oregon (UO) differs notably from other R1 (Carnegie “Very High Research Activity”) universities. The old post is here, but there’s no need to look at it. I’m now revisiting this … Continue reading Majors at [Your Ad Here] U. of Oregon, 2011-2021
On his blog today, Andrew Gelman posted a simple to state, clearly realistic, and surprisingly deep statistics puzzle sent to him by Ariel Rubinstein and Michele Piccione, from their paper, “Real People Aggregating Signals: An Experiment and a Short Story” Article Link: A very small proportion of the newborns in a certain country have a … Continue reading Three views of a statistics puzzle
This past term (Fall 2021) I taught a course on image analysis. It was a new course — not just new for me but completely new, though it grew out of an informal image analysis class I’ve taught “off the books” before and have posted about. The class went well, though I severely underestimated how … Continue reading Image Analysis: Reflections on a New Course
A terrible question to ask in science is unfortunately quite common: Does something affect something else? It seems innocuous enough — after all, one might want to know whether exposure to sunlight affects one’s risk of skin cancer, or whether the location of the moons of Jupiter affects the stock market. The answer, however, is … Continue reading Pushed around by stars
Our microbial ecology journal club, in addition to being fun and educational, is a limitless source of data presentation examples both wonderful and terrible. Here’s a recent example that drives me up the wall. It’s from a study [1] on correlations between pre-term births and various chemicals that may or may not be associated with … Continue reading When Should You Make a Plot of p-values? Never.
This week: the last of my commentaries on Makin and Orban de Xivry’s Common Statistical Mistakes! (Previous posts: #1-2, #3 , #4, #5, #6, #7, #8.) I’m lumping together comments on “Mistake #9” (Over-interpreting non-significant results) and “Mistake #10” (Correlation and causation), as well as concluding remarks, writing one long post instead of two or … Continue reading Comments on over-interpreting results, correlation and causation, and concluding remarks — “Ten common statistical mistakes…” #9-10
This week’s installment of comments on Makin and Orban de Xivry’s Common Statistical Mistakes deals with #8: Failure to Correct for Multiple Comparisons. (Previous posts: #1-2, #3 , #4, #5, #6, #7.) Makin and Orban de Xivry’s description is rather complex, but the error is a simple one. To illustrate: suppose we have a control … Continue reading Comments on “Failure to Correct for Multiple Comparisons” — “Ten common statistical mistakes…” #8
This week’s commentary on Makin and Orban de Xivry’s Common Statistical Mistakes covers #7: Flexibility of Analysis: p-Hacking. (Previous posts: #1-2, #3 , #4, #5, #6.) I feel like this has been discussed ad nauseum,* yet the problem still exists. The issue is that flexibility in how one analyzes data, even seemingly innocuous flexibility, can … Continue reading Comments on “p-Hacking (Flexibility of Analysis)” — “Ten common statistical mistakes…” #7
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The Eighteenth Elephant 