The Great Minds Journal Club discusses Westfall & Yarkoni (2016)

[Editorial note: The people and events described here are fictional. But the paper in question is quite real.] “Dearly Beloved,” The Graduate Student began. “We are gathered here to–” “Again?” Samantha interrupted. “Again with the Dearly Beloved speech? Can’t we just start a meeting like a normal journal club for once? We’re discussing papers here, … Continue reading The Great Minds Journal Club discusses Westfall & Yarkoni (2016)

the mysterious inefficacy of weather

I like to think of myself as a data-respecting guy–by which I mean that I try to follow the data wherever it leads, and work hard to suppress my intuitions in cases where those intuitions are convincingly refuted by the empirical evidence. Over the years, I’ve managed to argue myself into believing many things that … Continue reading the mysterious inefficacy of weather

the weeble distribution: a love story

“I’m a statistician,” she wrote. “By day, I work for the census bureau. By night, I use my statistical skills to build the perfect profile. I’ve mastered the mysterious headline, the alluring photo, and the humorous description that comes off as playful but with a hint of an edge. I’m pretty much irresistible at this … Continue reading the weeble distribution: a love story

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)

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?

The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this: Ruby for text processing and miscellaneous scripting; Ruby on Rails/JavaScript for web development; Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing; MATLAB for neuroimaging data analysis; R for statistical analysis; R for plotting … Continue reading The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

R, the master troll of statistical languages

Warning: what follows is a somewhat technical discussion of my love-hate relationship with the R statistical language, in which I somehow manage to waste 2,400 words talking about a single line of code. Reader discretion is advised. I’ve been using R to do most of my statistical analysis for about 7 or 8 years now–ever … Continue reading R, the master troll of statistical languages

Sixteen is not magic: Comment on Friston (2012)

UPDATE: I’ve posted a very classy email response from Friston here. In a “comments and controversies” piece published in NeuroImage last week, Karl Friston describes “Ten ironic rules for non-statistical reviewers”. As the title suggests, the piece is presented ironically; Friston frames it as a series of guidelines reviewers can follow in order to ensure … Continue reading Sixteen is not magic: Comment on Friston (2012)

large-scale data exploration, MIC-style

UPDATE 2/8/2012: Simon & Tibshirani posted a critical commentary on this paper here. See additional thoughts here. Real-world data are messy. Relationships between two variables can take on an infinite number of forms, and while one doesn’t see, say, umbrella-shaped data very often, strange things can happen. When scientists talk about correlations or associations between … Continue reading large-scale data exploration, MIC-style

Too much p = .048? Towards partial automation of scientific evaluation

Distinguishing good science from bad science isn’t an easy thing to do. One big problem is that what constitutes ‘good’ work is, to a large extent, subjective; I might love a paper you hate, or vice versa. Another problem is that science is a cumulative enterprise, and the value of each discovery is, in some … Continue reading Too much p = .048? Towards partial automation of scientific evaluation