**Mine Cetinkaya-Rundel, Duke University**

Just a couple years ago I would have answered the question “Why simulation based?” with the following:

- opportunity to introduce inference before (or without) discussing details of probability distributions
- conceptual understanding of p-values – both the “assume the null hypothesis is true” part and the “observed or more extreme” part

Being able to introduce computation as an essential tool for conducting statistical inference is a huge benefit of simulation based inference.

These are the reasons why in the first chapter of OpenIntro Statistics (

link), a textbook I co-authored, we decided to include a section on randomization tests. The Introductory Statistics with Randomization and Simulation (

link) textbook takes these ideas a step further and provides an introduction to statistical inference completely from a simulation based perspective. I believe these are important reasons for teaching simulation based inference, and many have already discussed them at length. However, for this post I’d like to focus on a lesser-discussed reason for teaching simulation based inference: it provides an opportunity to teach computation.

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