From cb45b0be45923b97d965ee116c7fb7a00954db60 Mon Sep 17 00:00:00 2001
From: Philip Waggoner <31326382+pdwaggoner@users.noreply.github.com>
Date: Tue, 19 Sep 2023 09:40:15 -0600
Subject: [PATCH] Update statistical_power.Rmd

---
 vignettes/statistical_power.Rmd | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/vignettes/statistical_power.Rmd b/vignettes/statistical_power.Rmd
index 9cb9cf67..6a663d1b 100644
--- a/vignettes/statistical_power.Rmd
+++ b/vignettes/statistical_power.Rmd
@@ -111,8 +111,11 @@ Given the simplicity of this example and the prevalence of Cohen's $d$, we will
 
 The first approach is the simplest. As previously hinted at, there is a vast literature on different effect size calculations for different applications. So, if you don't want to track down a specific one, or are unaware of options, you can simply pass the statistical test object to `effectsize()`, and either select the `type`, or leave it blank for "cohens_d", which is the default option. 
 
-```{r}
-effectsize(t, type = "cohens_d")
+```{r warning = FALSE}
+effectsize(
+  t, 
+  type = "cohens_d"
+)
 ```
 
 *Note*, users can easily store the value and/or CIs as you'd like via, e.g., `cohens_d <- effectsize(t, type = "cohens_d")[1]`.