Priors on effect size in A/B testing just saw this interesting applied-focused post by Kaiser Fung on non-significance in A/B testing. Kaiser was responding to a post by Ron Kohavi. I

Who were the business superstars of the 1970s? month, we said: Who are today’s heroes? Not writers or even musicians? No, our pantheon of culture heroes are: rich men, athletes, some movie

Inference for coronavirus prevalence by inverting hypothesis tests Toulis writes: The debate on the Santa Clara study actually me to think about the problem from a finite sample inference perspective. In this

The value of thinking about varying treatment effects: coronavirus example we discussed difficulties with the concept of average treatment effect. Part of designing a study is accounting for uncertainty in effect sizes. Unfortunately there

A Social Network Simulation In The Tidyverse „There is no way you know Thomas! What a coincidence! He’s my best friend’s saxophone teacher! This cannot be true. Here we are, at

Announcing Public Package Manager and v1.1.6 Today we are excited to release version 1.1.6 of RStudio Package Manager and announce This service builds on top of the work done

Understanding the “average treatment effect” number statistics and econometrics there’s lots of talk about the average treatment effect. I’ve often been skeptical of the focus on the average treatment effect,

Future-Proofing Your Data Science Team Photo by Brian McGowan on Unsplash This is a guest post from RStudio’s partner, Mango Solutions As RStudio’s Carl Howe recently discussed in his