diff --git a/analysis/final/R/analysis.R b/analysis/final/R/analysis.R
index 7aa598e..cf6b833 100644
--- a/analysis/final/R/analysis.R
+++ b/analysis/final/R/analysis.R
@@ -141,11 +141,18 @@ completion_time_stats <- function(download_times, meta) {
     p05 = quantile(completion_times, p = 0.05),
     p10 = quantile(completion_times, p = 0.10),
     p20 = quantile(completion_times, p = 0.20),
+    p25 = quantile(completion_times, p = 0.25),
     median = median(completion_times),
+    p75 = quantile(completion_times, p = 0.75),
     p80 = quantile(completion_times, p = 0.80),
     p90 = quantile(completion_times, p = 0.90),
     p95 = quantile(completion_times, p = 0.95),
     max = max(completion_times),
+    iqr = p75 - p25,
+    # This gives us roughly a 95% ci for comparing medians.
+    ci = (1.58 * iqr) / sqrt(n),
+    w_top = median + ci,
+    w_bottom = median - ci
   )
 }
 
diff --git a/analysis/final/static-dissemination.Rmd b/analysis/final/static-dissemination.Rmd
index 7ce627e..1c0cb91 100644
--- a/analysis/final/static-dissemination.Rmd
+++ b/analysis/final/static-dissemination.Rmd
@@ -67,6 +67,10 @@ We then plot the median by network size, and facet it by seeder ratio and file s
 
 ```{r fig.width = 10, warning=FALSE, message=FALSE}
 ggplot(benchmarks) +
+  geom_ribbon(aes(ymin = p25, ymax = p75, x = network_size),
+              fill = scales::alpha('blue', 0.5), col = 'lightgray') +
+  geom_point(aes(x = network_size, y = p25), col = 'darkgray', size=10.0, shape='-') +
+  geom_point(aes(x = network_size, y = p75), col = 'darkgray', size=10.0, shape='-') +
   geom_line(aes(x = network_size, y = median)) +
   geom_point(aes(x = network_size, y = median)) +
   ylab('median download time (seconds)') +