This post is motivated by Terry McGlynn’s thought provoking How do we move beyond an arbitrary statistical threshold? I have been struggling with the ideas explored in Terry’s post ever since starting my PhD 30 years ago, and its only been in the last couple of years that my own thoughts have begun to gel. This long marination period is largely because of my very classical biostatistical training. My PhD is from the Department of Anatomical Sciences at Stony Brook but the content was geometric morphometrics and Jim Rohlf was my mentor for morphometrics specifically, and multivariate statistics more generally.

A Harrell plot combines a forest plot of estimated treatment effects and uncertainty, a dot plot of raw data, and a box plot of the distribution of the raw data into a single plot. A Harrell plot encourages best practices such as exploration of the distribution of the data and focus on effect size and uncertainty, while discouraging bad practices such as ignoring distributions and focusing on \(p\)-values. Consequently, a Harrell plot should replace the bar plots and Cleveland dot plots that are currently ubiquitous in the literature.

Here is the motivating quote for this post, from Andrew Gelman’s blog post “Five ways to fix statistics”
I agree with just about everything in Leek’s article except for this statement: “It’s also impractical to say that statistical metrics such as P values should not be used to make decisions. Sometimes a decision (editorial or funding, say) must be made, and clear guidelines are useful.” Yes, decisions need to be made, but to suggest that p-values be used to make editorial or funding decisions—that’s just horrible.

This post is motivated by a twitter link to a recent blog post critical of the old but influential study An obesity-associated gut microbiome with increased capacity for energy harvest with impressive citation metrics. In the post, Matthew Dalby smartly used the available data to reconstruct the final weights of the two groups. He showed these final weights were nearly the same, which is not good evidence for a treatment effect, given that the treatment was randomized among groups.

An R doodle is a short script to check intuition or understanding. Almost always, this involves generating fake data. I might create an R doodle when I’m reviewing a manuscript or reading a published paper and I want to check if their statistical analysis is doing what the authors think it is doing. Or maybe I create it to help me figure out what the authors are doing. Or I might be teaching some method and I create an R doodle to help me understand how the method behaves given different input (fake) data sets.