A Gentle Introduction to Bayesian Posterior Predictive Checking for Single-Case Researchers

Grekov et al. (2026) wrote a how-to guide for single-case researchers. They show you how to use Bayesian posterior predictive checking. This means you make fake data from your model and compare it to your real data.

The authors walk through two real data sets step by step. They use simple graphs, not scary math. The whole process runs in free software.

The tutorial shows that a quick picture can tell you if your model fits. When the fake data look like the real data, you keep the model. When they do not, you fix the model.

The authors found this check catches mistakes that numbers alone miss. It works even with small data sets common in single-case work.

Dowdy et al. (2025) extend the same idea. They add real-time pooling across labs. Their method uses earlier cases to sharpen later ones, building on Grekov’s check-first mindset.

Manolov et al. (2022) give a different visual tool. They offer modified Brinley plots to judge replication. Grekov’s PPC graphs and Manolov’s plots both aim to make visual inference easier, but they solve different problems.

Tyrer et al. (2006) warned that visual and statistical views can clash. Grekov’s method bridges the gap by letting you see model fit visually before trusting the numbers.

You can run this check in under five minutes after you fit any Bayesian model. If the fake data bands do not line up with your real line, you know the model needs work before you write the report. It is a fast safety net that costs nothing and saves you from publishing a bad fit.