diff --git a/vignettes/epidist.Rmd b/vignettes/epidist.Rmd
index 216cf75c1..b2aa2e166 100644
--- a/vignettes/epidist.Rmd
+++ b/vignettes/epidist.Rmd
@@ -57,13 +57,16 @@ Finally, in Section \@ref(compare), we demonstrate that the fitted delay distrib
 
 If you would like more technical details, the `epidist` package implements models following best practices as described in @park2024estimating and @charniga2024best.
 
+Finally, to run this vignette yourself, you will need the `data.table`, `purrr` and `ggplot2` packages installed.
+Note that to work with outputs from `epidist` you do not need to use `data.table`: any tool of your preference is suitable.
+
 # Example data {#data}
 
-Data should be formatted as a [`data.table`](https://cran.r-project.org/web/packages/data.table/index.html) with the following columns for use within the `epidist` package:
+Data should be formatted as a [`data.table`](https://cran.r-project.org/web/packages/data.table/index.html) with the following columns for use within `epidist`:
 
-* `case`: 
-* `ptime`:
-* `stime`:
+* `case`: The unique case ID.
+* `ptime`: The time of the primary event.
+* `stime`: The time of the secondary event.
 
 Here we simulate data in this format, and in doing so explain the two main issues with observational delay data.
 <!-- Please see as of yet unwritten vignette for information about how to transform your data of other formats to the right format. -->
@@ -189,6 +192,11 @@ obs_cens_trunc_samp <-
   obs_cens_trunc[sample(seq_len(.N), sample_size, replace = FALSE)]
 ```
 
+Another issue, which `epidist` currently does not account for, is that sometimes only the secondary event might be observed, and not the primary event.
+For example, symptom onset may be reported, but start of infection unknown.
+Discarding events of this type leads to what are called ascertainment biases.
+Whereas each case is equally likely to appear in the sample above, under ascertainment bias some cases are more likely to appear in the data than others.
+
 With our censored, truncated, and sampled data, we are now ready to try to recover the underlying delay distribution using `epidist`.
 
 # Fit the model and compare estimates {#fit}