diff --git a/EDA.qmd b/EDA.qmd
index 76975c8..26de8cd 100644
--- a/EDA.qmd
+++ b/EDA.qmd
@@ -490,8 +490,9 @@ ggplot(mpg, aes(x = hwy, y = fct_reorder(class, hwy, median))) +
     What do you learn?
     How do you interpret the plots?
 
-5.  Compare and contrast `geom_violin()` with a faceted `geom_histogram()`, or a colored `geom_freqpoly()`.
-    What are the pros and cons of each method?
+5.  Create a visualization of diamond prices vs. a categorical variable from the `diamonds` dataset using `geom_violin()`, then a faceted `geom_histogram()`, and then a colored `geom_freqpoly()`.
+    Compare and contrast the three plots.
+    What are the pros and cons of each method of visualizing the distribution of a numerical variable based on the levels of a categorical variable?
 
 6.  If you have a small dataset, it's sometimes useful to use `geom_jitter()` to see the relationship between a continuous and categorical variable.
     The ggbeeswarm package provides a number of methods similar to `geom_jitter()`.