diff --git a/hacking_religion/chapter_1.qmd b/hacking_religion/chapter_1.qmd
index fe4c523..d77d724 100644
--- a/hacking_religion/chapter_1.qmd
+++ b/hacking_religion/chapter_1.qmd
@@ -1,4 +1,4 @@
-We'll get to the good stuff in a moment, but first we need to do a bit of setup. The code provided here is intended to set up your workspace and is also necessary for the `quarto` application we use to build this book. Quarto is an application which blends together text and blocks of code. You can ignore most of it for now, though if you're running the code as we go along, you'll definitely want to include these lines, as they create directories where your files will go as you create charts and extract data below and tells R where to find those files:
+In this chapter we're going to do some exciting things with census data. This is a very important dataset, often analysed, but much less frequently with regards to the subject of religion and almost never with the level of granularity you'll learn to work with over the course of this chapter. We'll get to the good stuff in a moment, but first we need to do a bit of setup. The code provided here is intended to set up your workspace and is also necessary for the `quarto` application we use to build this book. Quarto is an application which blends together text and blocks of code. You can ignore most of it for now, though if you're running the code as we go along, you'll definitely want to include these lines, as they create directories where your files will go as you create charts and extract data below and tells R where to find those files:
 
 ```{r}
 #| include: true
diff --git a/hacking_religion/chapter_2.qmd b/hacking_religion/chapter_2.qmd
index b144707..b7f5e4e 100644
--- a/hacking_religion/chapter_2.qmd
+++ b/hacking_religion/chapter_2.qmd
@@ -1,4 +1,4 @@
-# Getting into the nitty-gritty details
+# Different ways to measure religion using data science
 
 In this chapter, we'll explore the diverse variety of ways you can frame collecting data around religion. Before we dive into that all, however, you might be wondering, why does it all really matter? Can't we just use the census data and assume that's a reasonably accurate approximation? I'll explore the importance of getting the framing right, or better yet, working with data that seeks to unpack religious belonging, identity, and beliefs (or unbelief) in a variety of ways, but an example might serve to explain why this is important.
 
@@ -426,12 +426,14 @@ Let's start by assessing the correlation between these two elements of the data
 
 ::: {.callout-note collapse="true"}
 
-## Statistics 101: Correlation
+## Statistics 101: Correlation and Colonialism
 
 Content TBD.
 
 Discuss Pearson correlation coefficient
 
+Include commentary on the importance of Eugenics as a focal point for early statistics; highlight the turn away from significance testing in modern statistical analysis - note helpful commentary in https://nautil.us/how-eugenics-shaped-statistics-238014/
+
 :::
 
 To caluclate the correlation in R, you can use the function `cor()` like this:
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