yet another Python state machine (and why you might care)

TL;DR: I wrote a minimalistic state machine implementation in Python. You can find the code on GitHub. The rest of this post explains what a state machine is and why you might (or might not) care. The post is slanted towards scientists who are technically inclined but lack formal training in computer science or software … Continue reading yet another Python state machine (and why you might care)

In defense of In Defense of Facebook

A long, long time ago (in social media terms), I wrote a post defending Facebook against accusations of ethical misconduct related to a newly-published study in PNAS. I won’t rehash the study, or the accusations, or my comments in any detail here; for that, you can read the original post (I also recommend reading this … Continue reading In defense of In Defense of Facebook

In defense of Facebook

[UPDATE July 1st: I’ve now posted some additional thoughts in a second post here.] It feels a bit strange to write this post’s title, because I don’t find myself defending Facebook very often. But there seems to be some discontent in the socialmediaverse at the moment over a new study in which Facebook data scientists … Continue reading In defense of Facebook

There is no ceiling effect in Johnson, Cheung, & Donnellan (2014)

This is not a blog post about bullying, negative psychology or replication studies in general. Those are important issues, and a lot of ink has been spilled over them in the past week or two. But this post isn’t about those issues (at least, not directly). This post is about ceiling effects. Specifically, the ceiling … Continue reading There is no ceiling effect in Johnson, Cheung, & Donnellan (2014)

Big Data, n. A kind of black magic

The annual Association for Psychological Science meeting is coming up in San Francisco this week. One of the cross-cutting themes this year is “Big Data: Understanding Patterns of Human Behavior”. Since I’m giving two Big Data-related talks (1, 2), and serving as discussant on a related symposium, I’ve been spending some time recently trying to … Continue reading Big Data, n. A kind of black magic

estimating the influence of a tweet–now with 33% more causal inference!

Twitter is kind of a big deal. Not just out there in the world at large, but also in the research community, which loves the kind of structured metadata you can retrieve for every tweet. A lot of researchers rely heavily on twitter to model social networks, information propagation, persuasion, and all kinds of interesting … Continue reading estimating the influence of a tweet–now with 33% more causal inference!

then gravity let go

This is fiction. My grandmother’s stroke destroyed most of Nuremberg and all of Wurzburg. She was sailing down the Danube on a boat when it happened. I won’t tell you who she was with and what they were doing at the time, because you’ll think less of her for it, and anyway it’s not relevant … Continue reading then gravity let go

what exactly is it that 53% of neuroscience articles fail to do?

[UPDATE: Jake Westfall points out in the comments that the paper discussed here appears to have made a pretty fundamental mistake that I then carried over to my post. I’ve updated the post accordingly.] [UPDATE 2: the lead author has now responded and answered my initial question and some follow-up concerns.] A new paper in Nature Neuroscience … Continue reading what exactly is it that 53% of neuroscience articles fail to do?

strong opinions about data sharing mandates–mine included

Apparently, many scientists have rather strong feelings about data sharing mandates. In the wake of PLOS’s recent announcement–which says that, effective now, all papers published in PLOS journals must deposit their data in a publicly accessible location–a veritable gaggle of scientists have taken to their blogs to voice their outrage and/or support for the policy. … Continue reading strong opinions about data sharing mandates–mine included

What we can and can’t learn from the Many Labs Replication Project

By now you will most likely have heard about the “Many Labs” Replication Project (MLRP)–a 36-site, 12-country, 6,344-subject effort to try to replicate a variety of classical and not-so-classical findings in psychology. You probably already know that the authors tested a variety of different effects–some recent, some not so recent (the oldest one dates back … Continue reading What we can and can’t learn from the Many Labs Replication Project