Priors on effect size in A/B testing

https://statmodeling.stat.columbia.edu/2020/07/04/priors-on-effect-size-in-a-b-testing/I 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?

https://statmodeling.stat.columbia.edu/2020/07/03/who-were-the-business-superstars-of-the-1970s/Last 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

https://statmodeling.stat.columbia.edu/2020/07/02/inference-for-coronavirus-prevalence-by-inverting-hypothesis-tests/Panos 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

https://statmodeling.stat.columbia.edu/2020/07/01/the-value-of-thinking-about-varying-treatment-effects-coronavirus-example/Yesterday 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

https://www.statworx.com/de/blog/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

https://blog.rstudio.com/2020/07/01/announcing-public-package-manager/ Today we are excited to release version 1.1.6 of RStudio Package Manager and announce https://packagemanager.rstudio.com. This service builds on top of the work done

Understanding the “average treatment effect” number

https://statmodeling.stat.columbia.edu/2020/06/30/understanding-the-average-treatment-effect-number/In 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

https://blog.rstudio.com/2020/06/30/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