Monday, August 29, 2016

Everything is fucked: The syllabus, by Sanjay Srivastava (with links to articles)

This syllabus for PSY 607: Everything is Fuckedmade the rounds last week. The syllabus is for a course that purports that science is fucked. The course readings are a list of articles and books that hit on the limitations of statistics and research psychology (p-values, shortcomings of meta-analysis, misuse of mediation, replication crisis, etc.). PSY 607 isn't an actual class (as author/psychologist/blogger Srivastava explains in this piece from The Chronicle) but it does provide a fine reading list for understanding some of the current debates and changes in statistics and psychology.  Most of articles are probably too advanced for undergraduates but perfectly appropriate for teaching graduate students about our field and staying up to date as instructors of statistics.


Here is a link to the original blog post/syllabus. 

Monday, August 22, 2016

Harris' "How Big A Risk Is Acetaminophen During Pregnancy?"

This study, which found a link between maternal Tylenol usage during pregnancy and ADHD, has been making the rounds, particularly in the Academic Mama circles I move in.

Being pregnant is hard. For just about every malady, the only solution is to stay hydrated. With a compromised bladder.

But at least pregnant women have Tylenol for sore hips and bad backs. For a long time, this has been the only safe OTC pain reliever available to pregnant women. But a recent research article has cast doubt on this advice.

A quick read of this article makes it sound like you are cursing your child with a lifetime of ADHD if you take Tylenol. And this article has become click-bait fodder.

But these findings have some pretty big caveats. Harris published this reaction piece at NPR. It is a good teaching example of media hype vs. incremental scientific progress and the third (or fourth or fifth) variable problem. It also touches on absolute vs. relative risk. NOTE: There are well-documented concerns about Tylenol and liver damage. Also, more recent studies question its effectiveness, but those are teaching examples for another day.

Getting back to the current study: If you account for prenatal smoking, drinking, and pre-existing maternal psychiatric problems, the ADHD:Tylenol effect is either greatly weakened or goes away entirely.

And even if you don't account for those co-variates, the actual change that the study found was small. From the article:
It turns out you can't answer that question fully by reading the paper alone. You have to dig into the supplementary data tables posted online. The 20 percent to 45 percent increase is actually a small change. To pick one representative endpoint: Among women who had not taken the drug, 4.3 percent of their children registered an elevated score on the "SDQ total difficulties" test. Compare that with 6.3 percent of children born to women who did take the drug.

How to use in class:
-Absolute risk versus relative risk.
-Covariates.
-Crap science reporting.
-Introducing the topic of how hard it is to do medical research on pregnant women. No IRB under the sun would approve you to do a double blind study on anything that would potential damage a fetus or mamma! Hence, limited real knowledge on best practices for pregnant women until the shit hits the fan (see Accutane).

Monday, August 15, 2016

Ahn Le's "Gotta plot ‘em all!"

This example is a little out of my wheel house, but I'm putting it up here for those of you who teach more advanced UG stats or grad stats. I have never taught Principle Component Analysis. But Anh Le, PhD candidate at Duke, provides a detailed description of PCA in R AND does so using data that your advanced undergraduate/graduate students will enjoy: Pokemon. 

So, Le downloaded data for each of the 151 Pokemon (individual stats for the strengths and weakness of each Pokemon, and provided a link so that you can download the data as well). He even included the code he used to create his PCA via R AND he does a nice job talking the reader through his process and what the findings mean.




At 37, I didn't realize how much my traditionally-aged college students love Pokemon. Pokemon came up in my undergraduate I/O class three years ago, and I was shocked by how much nostalgia my then-20 year old students felt for the franchise. I think that it is certainly experiencing a revival now and this example would really catch (haha) students' attention.